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GTC On-Demand

Deep Learning and AI
Presentation
Media
Opening Keynote
Jensen Huang (NVIDIA)
The 2018 GTC opening keynote is delivered by the NVIDIA Founder and CEO, Jensen Huang, speaking on the future of computing. ...Read More

The 2018 GTC opening keynote is delivered by the NVIDIA Founder and CEO, Jensen Huang, speaking on the future of computing.

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Keywords:
Deep Learning and AI, GTC Silicon Valley 2018 - ID S8885
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AI Application Deployment and Inference
Presentation
Media
Deep Learning Implementers Panel: Field Insights for Accelerating Deep Learning Performance, Productivity and Scale
Tony Paikeday (NVIDIA), Scott Stephenson (Deepgram), Arun Subramaniyan (Baker Hughes, a GE Company), Neil Tenenholtz (MGH and BWH Center for Clinical Data Science)
This customer panel brings together A.I. implementers who have deployed deep learning at scale using NVIDIA DGX Systems. We'll focus on specific technical challenges we faced, solution design considerations, and best practices learned from i ...Read More

This customer panel brings together A.I. implementers who have deployed deep learning at scale using NVIDIA DGX Systems. We'll focus on specific technical challenges we faced, solution design considerations, and best practices learned from implementing our respective solutions. Attendees will gain insights such as: 1) how to set up your deep learning project for success by matching the right hardware and software platform options to your use case and operational needs; 2) how to design your architecture to overcome unnecessary bottlenecks that inhibit scalable training performance; and 3) how to build an end-to-end deep learning workflow that enables productive experimentation, training at scale, and model refinement.

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Keywords:
AI Application Deployment and Inference, AI and DL Business Track (high level), Data Center and Cloud Infrastructure, AI for Business, HPC and Supercomputing, GTC Silicon Valley 2018 - ID S8194
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Deploying Deep Neural Networks as a Service Using TensorRT and NVIDIA-Docker
Alec Gunny (NVIDIA), Prethvi Kashinkunti (NVIDIA)
Learn how you can utilize TensorRT and NVIDIA Docker to quickly configure and deploy a GPU-accelerated inference server and start gaining insights from your trained deep neural network (DNN) models. TensorRT is a high-performance tool for low-latency ...Read More
Learn how you can utilize TensorRT and NVIDIA Docker to quickly configure and deploy a GPU-accelerated inference server and start gaining insights from your trained deep neural network (DNN) models. TensorRT is a high-performance tool for low-latency, high-throughput DNN inference. The latest release of TensorRT introduces a novel, framework-agnostic network definition format called universal framework format, which allows TensorRT to support and optimize DNN models trained in multiple deep learning frameworks. We'll leverage the TensorRT Python API to create a lightweight Python Flask application capable of serving multiple DNN models trained using TensorFlow, PyTorch, and Caffe, and also discuss how to containerize this inference service using NVIDIA Docker for ease of deployment at scale. This session will consist of a lecture, live demos, and detailed instructions.  Back
 
Keywords:
AI Application Deployment and Inference, Tools and Libraries, Data Center and Cloud Infrastructure, GTC Silicon Valley 2018 - ID S8495
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Putting AI to Work in an Enterprise: Deep Learning as a Service (Presented by IBM)
Nick Werstiuk (IBM)
Now that Deep learning has moved out of the lab and into production, how do you provide training environments to all your internal customers working across business units with different requirements and avoid provisioning separate clusters? IBM has a ...Read More
Now that Deep learning has moved out of the lab and into production, how do you provide training environments to all your internal customers working across business units with different requirements and avoid provisioning separate clusters? IBM has applied decades of HPC experience to build a production ready learning stack, including servers accelerated with NVIDIA GPUs, workload and resource management software, ready to use open source frameworks and it's all covered by IBM support. The solution provides a secure multi-tenant environment so multiple data scientists can share a common set of resources, eliminating silos, while running multiple instances of the same or different applications. The deep learning effort is enhanced with end-to-end pipeline support from data ingestion and preparation, through model training and tuning, to inference. In this session, we will explore what an enterprise deep learning environment looks like and provide insights into the unique IBM value for accelerating the use of deep learning across a wide variety of industries.  Back
 
Keywords:
AI Application Deployment and Inference, GTC Silicon Valley 2018 - ID S81049
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VACnet: Using Deep Learning to Combat Cheating in 'Counter-Strike: Global Offensive'
John McDonald (Valve)
We'll delve into the nuts and bolts of how Valve has utilized deep learning to combat cheating in "Counter-Strike: Global Offensive." We'll cover total system details, from the high-level server architecture to the low-level features fed ...Read More
We'll delve into the nuts and bolts of how Valve has utilized deep learning to combat cheating in "Counter-Strike: Global Offensive." We'll cover total system details, from the high-level server architecture to the low-level features fed into the AI. Deep learning has proven to be very effective at identifying cheating behavior without any client-side instrumentation, making it robust against malicious attack by cheaters and cheat vendors. By retraining regularly, the network continues to evolve, picking up new cheating behaviors within hours of their appearance. As a result of this approach, certain types of cheats have been reduced by a factor of 100.  Back
 
Keywords:
AI Application Deployment and Inference, AI for Gaming, GTC Silicon Valley 2018 - ID S8732
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Adopting Artificial Intelligence Technologies in Networking (Presented by Cisco)
Hugo Latapie (Cisco)
This talk will provide an overview of what is happening in the world of artificial intelligence as it relates to networking, IT infrastructure, and IoT technologies. We will broadly cover AI topics ranging from machine learning and deep learning to s ...Read More
This talk will provide an overview of what is happening in the world of artificial intelligence as it relates to networking, IT infrastructure, and IoT technologies. We will broadly cover AI topics ranging from machine learning and deep learning to symbolic AI. Applied AI as well as general AI and their hybrids are all critical in solving many of today's complex long tail problems in real-time. Just as the capabilities, business opportunities, and positive benefits of AI are growing at a seemingly exponential rate so are the security vulnerabilities, failure modes, and potential adverse business impacts. We will discuss new hybrid neural symbolic approaches that promise to address these issues while simultaneously opening the door to powerful systems that dynamically learn and reason at multiple levels of abstraction, from raw data to high-level symbolic reasoning. We will cover use cases and solutions ranging from smart city, transportation, manufacturing, to security and networking.  Back
 
Keywords:
AI Application Deployment and Inference, Advanced AI Learning Techniques (incl. GANs and NTMs), GTC Silicon Valley 2018 - ID S8971
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Accelerating AI Adoption and Impact (Presented by Dell EMC)
Jay Boisseau (Dell)
Attendees will learn and understand why AI techniques are so powerful, why developing and deploying optimal AI solutions is complex, why using AI techniques effectively is still difficult--and what Dell Technologies is doing to remove these difficult ...Read More
Attendees will learn and understand why AI techniques are so powerful, why developing and deploying optimal AI solutions is complex, why using AI techniques effectively is still difficult--and what Dell Technologies is doing to remove these difficulties and bring easier, effective AI to everyone. Dell Technologies includes seven companies with a comprehensive portfolio of technology products, services and solutions for global industry, government, and education markets, and aims to be the leader in designing and delivering the best AI solutions for every customer, of every type and scale. From Dell Precision workstations for developers and Gateways for edge sensors, to Dell EMC GPU-optimized PowerEdge Servers and Ready Solutions for Deep Learning and hybrid cloud offerings, Dell is leveraging its leadership in technology and in enterprise relationships to design a world-class portfolio of AI solutions for diverse customer workloads, requirements and objectives. This presentation will cover AI and deep learning in an enterprise context, including customer challenges and needs, and then discuss Dell AI solutions and strategy to empower people to use AI rapidly and effectively.  Back
 
Keywords:
AI Application Deployment and Inference, GTC Silicon Valley 2018 - ID S81046
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Deep Learning of Railway Track Faults using GPUs
Nathalie Rauschmayr (CSEM (Swiss Center for Electronics and Microtechnology))
Swiss Federal Railways (SBB) operate a 'diagnosis' train fitted with multiple high-resolution cameras that obtain images of tracks - all while traveling at a speed of 75 mph. Current data processing software conducted in real time on the train prod ...Read More
Swiss Federal Railways (SBB) operate a 'diagnosis' train fitted with multiple high-resolution cameras that obtain images of tracks - all while traveling at a speed of 75 mph. Current data processing software conducted in real time on the train produces  a too high rate of false positives/negatives to the extent that railway experts still need to go on the track to physically inspect anomalies. This is not only very dangerous, but sometimes even impossible and in addition it requires a lot of human labor. We describe how deep learning technologies have been developed to massively improve the automatic detection and classification of railway faults. This is not just a nice-to-have, but rather a must-have in order to ensure the safety of future rail transportation.  Back
 
Keywords:
AI Application Deployment and Inference, Industrial Inspection, GTC Silicon Valley 2018 - ID S8944
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IBM PowerAI: Realizing Business Value with Machine Learning (Presented by IBM)
Adel El-Hallak (IBM)
There is no shortage of hype around AI, but realizing value through machine and deep learning comes with its challenges. IBM PowerAI removes the inhibitors across each stage of a workflow, allowing enterprises to rapidly realize business value with A ...Read More
There is no shortage of hype around AI, but realizing value through machine and deep learning comes with its challenges. IBM PowerAI removes the inhibitors across each stage of a workflow, allowing enterprises to rapidly realize business value with AI.  Back
 
Keywords:
AI Application Deployment and Inference, GTC Silicon Valley 2018 - ID S81048
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Monitoring Honey Bee Health Using TensorRT and Microsoft Cognitive Toolkit
Jacqueline Cenci-McGrody (NVIDIA), Anusua Trivedi (Microsoft)
We'll take a deep dive into honey bee hive health monitoring with NVIDIA's TX2, TensorRT (a high-performance deep learning inference optimizer), Kineticas insight engine running on DGX-1/DGXStaion, and Microsoft Cognitive Toolkit to rapidly o ...Read More
We'll take a deep dive into honey bee hive health monitoring with NVIDIA's TX2, TensorRT (a high-performance deep learning inference optimizer), Kineticas insight engine running on DGX-1/DGXStaion, and Microsoft Cognitive Toolkit to rapidly optimize, validate, and deploy trained neural networks for inference. In recent years, the media has reported that bees seem to be dying at an unprecedented rate. We'll explore how new accelerated analytics technologies and their corresponding compute platforms can deliver game-changing possibilities for innovation as we follow a honey bee farm scientist in California, who agreed to field test this real-time monitoring solution with her beehives.  See first-hand how adaptable and accessible these complex, cutting-edge technologies have become and how we can use intelligent monitoring technologies to help rescue the honey bee in the real-world environmental analytics opportunity.  Back
 
Keywords:
AI Application Deployment and Inference, Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S8508
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Practical Application of Deep Learning in Smart Factory: Visual Inspection System of Semiconductor Laser
Hiroyuki Kusaka (Fujikura Ltd)
Fujikura is pushing forward of implementation of the smart factory with AI and IoT for improving the productivity and production quality. In this presentation, we will present visual inspection system incorporating deep learning in the production pro ...Read More
Fujikura is pushing forward of implementation of the smart factory with AI and IoT for improving the productivity and production quality. In this presentation, we will present visual inspection system incorporating deep learning in the production process of semiconductor lasers. Not only OK/NG classification, but also multiple NG mode classification was performed. The inspection accuracy of 95 % that is equivalent to skilled workers' accuracy was achieved by optimizing the data set and the hyper parameters of a CNN model. The activation map was used for reliability and validity assurance. We will present the difficulty in our practical application in manufacturing industry, such as the small number of some category and small defect/chip size ratio, and also introduce our countermeasures.  Back
 
Keywords:
AI Application Deployment and Inference, Industrial Inspection, GTC Silicon Valley 2018 - ID S8911
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Deep Learning for Heliophysics
Mark Cheung (Lockheed Martin Solar & Astrophysics Laboratory)
NASA's heliophysics division operates a fleet of spacecraft, the so-called Heliophysics System Observatory, to monitor the Sun's activity and how its changes drive space weather in interplanetary space and in the near-Earth environment. We'll pres ...Read More
NASA's heliophysics division operates a fleet of spacecraft, the so-called Heliophysics System Observatory, to monitor the Sun's activity and how its changes drive space weather in interplanetary space and in the near-Earth environment. We'll present case studies of how a number of challenging problems encountered in heliophysics can be tackled using deep learning: spectropolarimetric inversions for measuring the magnetic field on the solar surface, and mega-Kelvin thermometry of the Sun's corona by using a deep neural network to solve a compressed sensing problem. These low-cost solutions make possible new concepts for deep space missions for space weather monitoring. Some of the work in this presentation was made possible by NASA's Frontier Development Lab, a public-private partnership between the agency and industry partners (including the SETI Institute, NVIDIA, IBM, Intel, kx & Lockheed Martin), whose mission is to use artificial intelligence to tackle problems related to planetary defense and heliophysics.  Back
 
Keywords:
AI Application Deployment and Inference, Accelerated Analytics, Astronomy and Astrophysics, GTC Silicon Valley 2018 - ID S8222
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How AI Technology Lifts the Ads Business in JD.com
Juyan Song (NVIDIA), YAN YAN (JD.com)
Deep learning and reinforcement learning are widely used in ads products of JD.com, e.g. ranking model in recommender systems, bidding model in ad exchange business and automatic ads review systems. These technologies have brought great benefits to J ...Read More
Deep learning and reinforcement learning are widely used in ads products of JD.com, e.g. ranking model in recommender systems, bidding model in ad exchange business and automatic ads review systems. These technologies have brought great benefits to JD.com and all of them are built on Nvidia GPUs.  Back
 
Keywords:
AI Application Deployment and Inference, Consumer Engagement and Personalization, GTC Silicon Valley 2018 - ID S81016
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Low-Latency GPU Accelerated Inferencing with TensorRT
Prethvi Kashinkunti (NVIDIA)
Come learn how you can optimize the deployment of your trained neural networks using the GPU-accelerated inferencing library called TensorRT. TensorRT is a high-performance tool for low-latency, high-throughput deep neural network (DNN) inference tha ...Read More
Come learn how you can optimize the deployment of your trained neural networks using the GPU-accelerated inferencing library called TensorRT. TensorRT is a high-performance tool for low-latency, high-throughput deep neural network (DNN) inference that runs on NVIDIA GPUs. The latest release of TensorRT introduces a novel, framework-agnostic network definition format called universal framework format, allowing TensorRT to support and optimize DNN models trained in multiple deep learning frameworks like Caffe and TensorFlow. It also provides the capability to run inference at reduced precision, giving developers the ability to take advantage of new GPU hardware features like the Volta Tensor Core architecture. This session will be a combination of lecture and live demos.  Back
 
Keywords:
AI Application Deployment and Inference, Tools and Libraries, Performance Optimization, Data Center and Cloud Infrastructure, GTC Silicon Valley 2018 - ID S8496
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Latest Tools and Techniques for Training and Deploying Deep Neural Networks in Educational Environments
Joseph Bungo (NVIDIA), Dmytro Lituiev (UC Berkeley and UCSF), Craig Morioka (UCLA)
Craig Morioka, UCLA Adjunct Associate Professor of Radiological Sciences, and Dima Lituiev, Postdoctoral Scholar at the University of California San Francisco, Institute for Computational Health Sciences, will discuss how they empower their fellow fa ...Read More
Craig Morioka, UCLA Adjunct Associate Professor of Radiological Sciences, and Dima Lituiev, Postdoctoral Scholar at the University of California San Francisco, Institute for Computational Health Sciences, will discuss how they empower their fellow faculty, staff, and students with the latest techniques in training and deploying deep neural networks through NVIDIAs Deep Learning Institute (DLI) University Ambassador Program - a new AI and Deep Learning education enablement program for universities. This will include a dive into the benefits of an online learning platform, which uses GPUs in the cloud, by stepping through the DLIs online Image Segmentation and Radiomics labs. The Image Segmentation lab leverages an example from medical image analysis where it is often important to separate pixels corresponding to different types of tissue or cells for the purposes of diagnostics and treatment planning. Dima uses image segmentation in his research to facilitate diagnostics of kidney rejection by analyzing histological slides from patients with kidney transplants. We will explore how the Tensorflow code is structured and how the Tensorboard tool can be used to visualize structure and training dynamics of segmentation models. The focus of the Radiomics lab is detection of the 1p19q co-deletion biomarker using deep learning - specifically convolutional neural networks using the Keras and TensorFlow computing frameworks. Attendees will also learn how they can apply to become a DLI University Ambassador and bring the latest in Deep Learning and AI education to their academic communities.    Back
 
Keywords:
AI Application Deployment and Inference, Deep Learning and AI Frameworks, AI and DL Business Track (high level), GTC Silicon Valley 2018 - ID S8823
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Protecting Pulsed High-Power Lasers with Real-Time Image Classification
Jeffrey Kelling (Helmholtz-Zentrum Dresden - Rossendorf)
Learn how to combine computer vision techniques and deep learning to improve the sensitivity of a real-time, GPU-powered safety system. In petawatt laser systems, firing at 10 Hz, suddenly appearing scatterers can damage components. Spreading of dama ...Read More
Learn how to combine computer vision techniques and deep learning to improve the sensitivity of a real-time, GPU-powered safety system. In petawatt laser systems, firing at 10 Hz, suddenly appearing scatterers can damage components. Spreading of damage can be avoided by suspending operation immediately on occurrence of such an event. We'll present our approach for the automatic detection of critical failure states from intensity profiles of the laser beam. By incorporating quick feature detection and learned heuristics for feature classification, both real-time constraints and limited available training data are accommodated. Localization of triggering feature is crucial for when the problem is located in non-sensitive sections and will not be removed from the beam in production.  Back
 
Keywords:
AI Application Deployment and Inference, Advanced AI Learning Techniques (incl. GANs and NTMs), Computer Vision, GTC Silicon Valley 2018 - ID S8330
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Deep Learning Demystified
William Ramey (NVIDIA)
What is Deep Learning? In what fields is it useful? How does it relate to artificial intelligence? We'll discuss  deep learning and why this powerful new technology is getting so much attention, learn how deep neural networks are traine ...Read More

What is Deep Learning? In what fields is it useful? How does it relate to artificial intelligence? We'll discuss  deep learning and why this powerful new technology is getting so much attention, learn how deep neural networks are trained to perform tasks with super-human accuracy, and the challenges organizations face in adopting this new approach. We'll also cover some of the best practices, software, hardware, and training resources that many organizations are using to overcome these challenges and deliver breakthrough results.

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Keywords:
AI Application Deployment and Inference, Deep Learning and AI Frameworks, Deep Learning and AI, GTC Silicon Valley 2018 - ID S8669
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Enabling Deep Learning Applications in Radio Frequency Systems
John Ferguson (Deepwave Digital)
Artificial intelligence has made great strides in many technology sectors, however, it has yet to impact the design and applications of radio frequency (RF) and wireless systems. This is primarily due to the industry''s preference towards field progr ...Read More
Artificial intelligence has made great strides in many technology sectors, however, it has yet to impact the design and applications of radio frequency (RF) and wireless systems. This is primarily due to the industry''s preference towards field programmable gate array (FPGA) systems. Conversely, the deep learning revolution has been fueled by GPUs and the ease with which they may be programmed for highly parallel computations. The next generation RF and wireless technology will require wide-band systems capable of real-time machine learning with GPUs. Working with strategic partners, we''ve designed a software configurable wide-band RF transceiver system capable of performing real-time signal processing and machine learning with a Jetson TX2. We discuss system performance, collection of RF training data, and the software used by the community to create custom applications. Additionally, we''ll present data demonstrating applications in the field of RF machine learning and deep learning.  Back
 
Keywords:
AI Application Deployment and Inference, NVIDIA Inception Program, Cyber Security, IoT, Robotics & Autonomous Machines, GTC Silicon Valley 2018 - ID S8375
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Breaking the Barriers to AI-Scale in the Enterprise
Charles Boyle (NVIDIA)
Organizations everywhere want to AI-infuse every aspect of their business, but need a platform that delivers the scale and flexibility to fit both IT operational constraints, as well as workload performance demanded by data scientists. Attend this se ...Read More
Organizations everywhere want to AI-infuse every aspect of their business, but need a platform that delivers the scale and flexibility to fit both IT operational constraints, as well as workload performance demanded by data scientists. Attend this session to get see the latest advancements in scaling in GPU servers and deep learning software, and hear how the latest solutions from NVIDIA solve your biggest AI platform challenges  Back
 
Keywords:
AI Application Deployment and Inference, Data Center and Cloud Infrastructure, AI and DL Research, GTC Silicon Valley 2018 - ID S8196
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Continuous Delivery of AI Applications
Asif Khan (Amazon)
Deep learning systems are usually developed by data scientists, who are good at mathematics and computer science. But to deploy and operationalize these models for broader use, you need the devops mindset and tools. We''ll show you how to connect the ...Read More
Deep learning systems are usually developed by data scientists, who are good at mathematics and computer science. But to deploy and operationalize these models for broader use, you need the devops mindset and tools. We''ll show you how to connect the workflow between the data scientists and devops. We''ll also explore basic continuous integration and delivery concepts and how they can be applied to deep learning models. Using a number of AWS services, we''ll showcase how you can take the output of a deep learning model and deploy it to perform predictions in real time with low latency and high availability. In particular, we''ll showcase the ease of deploying DL to predict functions using Apache MXNet (a deep learning library), Amazon ECS, Amazon S3, and Amazon ECR, Amazon developer tools, and AWS CloudFormation.  Back
 
Keywords:
AI Application Deployment and Inference, GTC Silicon Valley 2018 - ID S8173
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Defect Inspection from Scratch to Production
Kuan-Liang (Andrew) Liu (NVIDIA), Sheng-Ting Shen (NVIDIA)
In order to fulfill customer''s requirement, companies have to guarantee the quality of delivered products, which can often be achieved only by manually inspection of the finished product. Since human-based defect inspection and classification are ti ...Read More
In order to fulfill customer''s requirement, companies have to guarantee the quality of delivered products, which can often be achieved only by manually inspection of the finished product. Since human-based defect inspection and classification are time-consuming and the results vary by individuals, automatic defect detection and classification has the potential to reduce the cost of quality assurance significantly. In this talk, we will demonstrate how to utilize deep learning algorithms, i.e., Fully Convolutional Neural Network to build a general defect inspection and classification model. We will also share experiences on how to effectively collect labelling data, deal with imbalance data, and also how to optimize the model in terms of latency and throughput with TensorRT before deploy the model to the production line.  Back
 
Keywords:
AI Application Deployment and Inference, Industrial Inspection, IoT, Robotics & Drones, Robotics & Autonomous Machines, GTC Silicon Valley 2018 - ID S8682
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Revisiting the TurboCharged Test Toolbox: VR, Robotics, and More DL
Martina Sourada (NVIDIA)
Last year, we began to see promising results of applying Deep Learning in an unexpected space: hardware QA. Fast forward +365, and the efforts have been to expand on what we''ve learned, push the technology broader and into other areas that will ulti ...Read More
Last year, we began to see promising results of applying Deep Learning in an unexpected space: hardware QA. Fast forward +365, and the efforts have been to expand on what we''ve learned, push the technology broader and into other areas that will ultimately aid in our greatest challenge: testing at scale. In this session we will highlight a new piece of the problem we are tackling: VR. We will introduce methodologies for not only addressing the unique problems that VR testing presents, but will also showcase some of the other test process areas where we are applying other Deep Learning models to gain efficiency in our overall production pipeline. From using DL on our bug mining to create a quicker path from tester to developer and back, to analysis on end user issues as a method for task automation, explore how we are enabling speed, accuracy and efficiency.  Back
 
Keywords:
AI Application Deployment and Inference, Virtual Reality and Augmented Reality, Tools and Libraries, Graphics and AI, AI for Gaming, GTC Silicon Valley 2018 - ID S8262
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Deep Learning Infrastructure for Autonomous Vehicles
Pradeep Gupta (NVIDIA)
We''ll introduce deep learning infrastructure for building and maintaining autonomous vehicles, including techniques for managing the lifecycle of deep learning models, from definition, training and deployment to reloading and life-long ...Read More

We''ll introduce deep learning infrastructure for building and maintaining autonomous vehicles, including techniques for managing the lifecycle of deep learning models, from definition, training and deployment to reloading and life-long learning. DNN autocurates and pre-labels data in the loop. Given data, it finds the best run-time optimized deep learning models. Training scales with data size beyond multi-nodes. With these methodologies, one takes only data from the application and feeds DL predictors to it. This infrastructure is divided into multiple tiers and is modular, with each of the modules containerized to lower infrastructures like GPU-based cloud infrastructure.

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Keywords:
AI Application Deployment and Inference, Data Center and Cloud Infrastructure, Autonomous Vehicles, Autonomous Machines, GTC Silicon Valley 2018 - ID S8531
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Digital Twin for the Railway Network
Dattaraj Rao (General Electric)
We describes concept of Digital Twin with respect to the Railway Network. Railroad customers across the world manage thousands of miles of Track infrastructure that consists of the Rails, Ballast, Ties, Bridges, Tunnels, Wayside equipment, etc. This ...Read More
We describes concept of Digital Twin with respect to the Railway Network. Railroad customers across the world manage thousands of miles of Track infrastructure that consists of the Rails, Ballast, Ties, Bridges, Tunnels, Wayside equipment, etc. This talk demonstrates a new approach to Track infrastructure monitoring that GE is piloting for customers using the concept of Digital Twin for network. Using an offline GPU infrastructure Deep Learning models are created and trained on large volumes of video data to learn the state of healthy Track and predict anomalies. During the talk, real customer use-case videos will be shown that demonstrate Analytics on videos from Locomotive-mounted cameras with Deep Learning models to calculate health index and display on a map for driving Maintenance decisions.  Back
 
Keywords:
AI Application Deployment and Inference, Computer Vision, GTC Silicon Valley 2018 - ID S8614
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How Deep Learning Could Predict Weather Events
Sa-Kwang Song (Korea Institute of Science and Technology)
How do meteorologists predict weather or weather events such as hurricanes, typhoons, and heavy rain? Predicting weather events were done based on supercomputer (HPC) simulations using numerical models such as WRF, UM, and MPAS. But recently, many de ...Read More
How do meteorologists predict weather or weather events such as hurricanes, typhoons, and heavy rain? Predicting weather events were done based on supercomputer (HPC) simulations using numerical models such as WRF, UM, and MPAS. But recently, many deep learning-based researches have been showing various kinds of outstanding results. We'll introduce several case studies related to meteorological researches. We'll also describe how the meteorological tasks are different from general deep learning tasks, their detailed approaches, and their input data such as weather radar images and satellite images. We'll also cover typhoon detection and tracking, rainfall amount prediction, forecasting future cloud figure, and more.  Back
 
Keywords:
AI Application Deployment and Inference, Climate, Weather, Ocean Modeling, Computer Vision, HPC and AI, GTC Silicon Valley 2018 - ID S8816
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Containerizing Deep Learning with Singularity
Nishanth Dandapanthula (Dell EMC)
We'll talk about how to use Singularity to containerize deep learning applications. We'll provide compelling reasons to choose Singularity over Docker. We'll cover deep learning frameworks, including TensorFlow, NV-Caffe, MXNet, and others. We'll ...Read More
We'll talk about how to use Singularity to containerize deep learning applications. We'll provide compelling reasons to choose Singularity over Docker. We'll cover deep learning frameworks, including TensorFlow, NV-Caffe, MXNet, and others. We'll present the current challenges and workarounds when using Singularity in a HPC cluster. We'll compare the performance of Singularity to bare-metal systems.  Back
 
Keywords:
AI Application Deployment and Inference, HPC and AI, GTC Silicon Valley 2018 - ID S8368
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ANI-AL: Universal Deep Learning Potentials for Organic Molecules and Materials
Justin Smith (University of Florida)
We'll introduce ANI-AL molecular potentials, which are deep learning based potential energy functions for the fast and accurate prediction of quantum mechanical energies and forces of molecular systems. Thanks to GPU acceleration of training and inf ...Read More
We'll introduce ANI-AL molecular potentials, which are deep learning based potential energy functions for the fast and accurate prediction of quantum mechanical energies and forces of molecular systems. Thanks to GPU acceleration of training and inference, we successfully implement an automated sampling method that borrows techniques from active learning to automatically drive the systematic improvement of ANI-AL potentials. We'll also present results from applications of the ANI-AL potential in various problems relating to computational chemistry, such as molecular structure optimization, reaction path prediction, vibrational frequency calculation, and molecular dynamics simulations.  Back
 
Keywords:
AI Application Deployment and Inference, Computational Biology and Chemistry, GTC Silicon Valley 2018 - ID S8827
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Accelerating Large-Scale Video Surveillance for Smart Cities with TensorRT
Shounan An (SK Telecom)
We'll discuss a detailed scale-up method for accelerating deep learning-based object detection inference engine with INT8 by using NVIDIA's TensorRT. Previously, converting convolutional neural networks (CNNs) from 32-bit floating-point arithmetic ...Read More
We'll discuss a detailed scale-up method for accelerating deep learning-based object detection inference engine with INT8 by using NVIDIA's TensorRT. Previously, converting convolutional neural networks (CNNs) from 32-bit floating-point arithmetic (FP32) to 8-bit integer (INT8) for classification tasks has been researched. However, there is no solid work for accelerating CNN-based object detection tasks. We'll explain how to accelerate YOLO-v2, the state-of-the-art CNN-based object detector with TensorRT using INT8. We improved YOLO-v2 network for better acceleration and more accurate for surveillance and named our network SIDNet. We verified SIDNet on several benchmark object detection and intrusion detection datasets and confirmed that SIDNet with INT8 has only 1% accuracy drop compared with FP32 mode and is 5x faster than the original YOLO-v2 on NVIDIA Tesla P40.  Back
 
Keywords:
AI Application Deployment and Inference, Telecom Industry Solutions, Deep Learning and AI Frameworks, Computer Vision, Robotics & Autonomous Machines, GTC Silicon Valley 2018 - ID S8296
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Deep Learning for Industrial Inspection Analysis
Paul Baines (Wise.io from GE Digital)
We'll show how GE combines extensive domain knowledge with modern deep learning techniques to build intelligent pipeline inspection systems. GE builds a variety of industrial inspection equipment from ultrasonic pipeline inspection gauges to large-s ...Read More
We'll show how GE combines extensive domain knowledge with modern deep learning techniques to build intelligent pipeline inspection systems. GE builds a variety of industrial inspection equipment from ultrasonic pipeline inspection gauges to large-scale CT scanners. As historical producers of hardware, GE is now leading the transformation of the industrial space by building intelligent ecosystems around industrial equipment and processes. Challenges in this space include the esoteric domain-specific nature of the data, as well as the risk averse nature of the industry. However, by leveraging deep learning on large amounts of inspection data, we have been able to build a production system that enhances the reliability and consistency of the inspection process.  Back
 
Keywords:
AI Application Deployment and Inference, Industrial Inspection, GTC Silicon Valley 2018 - ID S8657
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AI and DL Business Track (high level)
Presentation
Media
From Dark Matter Detection to Deep Learning in Enterprise
Scott Stephenson (Deepgram)
Advancements in deep learning are enabling enterprise companies to make meaningful impacts to bottom-line profits. Enterprises capture thousands of hours of customer phone call recordings per day. This voice data is extremely valuable because it cont ...Read More
Advancements in deep learning are enabling enterprise companies to make meaningful impacts to bottom-line profits. Enterprises capture thousands of hours of customer phone call recordings per day. This voice data is extremely valuable because it contains insights that the business can use to improve customer experience and operations. We'll follow Deepgram CEO Dr. Scott Stephenson's path from working in a particle physics lab two miles underground to founding a deep learning company for voice understanding. We'll describe applications of cutting-edge AI techniques to make enterprise voice datasets mineable for valuable business insights. Companies today use these insights to drive the bottom line.  Back
 
Keywords:
AI and DL Business Track (high level), Telecom Industry Solutions, Speech and Language Processing, NVIDIA Inception Program, GTC Silicon Valley 2018 - ID S8274
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The Face Will State The Case
Lisa Hammitt (Beseeq), Rebecca Krauthamer (Beseeq)
We have all heard about Facial Expression and Recognition Systems (FERS) and emotion capture but curiosity looms large. Is it training sets born of Generative Adversarial Networks (GANs) along with GPU architectures that will catapult this technolog ...Read More
We have all heard about Facial Expression and Recognition Systems (FERS) and emotion capture but curiosity looms large. Is it training sets born of Generative Adversarial Networks (GANs) along with GPU architectures that will catapult this technology forward? To be sure, but, something much deeper - a revolution within Computer Science programs in the schools - will accelerate its arrival in consumer platforms. It's called Social Signal Processing and women technologists have a competitive advantage in inventing and enhancing the deep learning algorithms that will fuel it. Come and listen to an industry veteran with 28 years in Artificial Intelligence, including her driving Watson into consumer platforms and a graduate of Stanford University, bolstered by her solid research in Symbolic Systems, discuss their patent-pending technology in the exciting area of Social Signal Processing and FERS. They are both frequent speakers on the ethics of AI usage and will offer their thoughts about how this new class of technology offers a new deal for women to shape the future of AI.  Back
 
Keywords:
AI and DL Business Track (high level), AI and DL Research, GTC Silicon Valley 2018 - ID S8939
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Matching DS Organizational Maturity to DS Skills to Optimally Grow Your Team
Jesse Spencer-Smith (HCA Healthcare)
An organization''s data science needs change dramatically as they move through stages of data science maturity--their ability to consume, adopt, and deploy advanced analytics solutions. Understanding the maturity stage of your organization will help ...Read More
An organization''s data science needs change dramatically as they move through stages of data science maturity--their ability to consume, adopt, and deploy advanced analytics solutions. Understanding the maturity stage of your organization will help you choose projects that can bring value, grow your ability to derive greater value in the future, and help you make good decisions when growing your data science team. A data scientist might be a journeyman model builder, or a data scientist consultant, or a software engineer, or a developer of new deep learning algorithms. The data scientist that would be successful in a mature organization may well fail in an organization new to data science. Hiring and growing data scientists based on skill sets in line with your data science maturity stage and maximizes your probability of success. We''ll discuss a framework to determine your level of data science readiness, explore a tool to assess the skill sets of data scientists, and find which skills can maximize your organization''s probability of success at each stage.  Back
 
Keywords:
AI and DL Business Track (high level), GTC Silicon Valley 2018 - ID S8954
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Real-Time Genetic Analysis Enabled by GPU
Wayne Thompson (SAS)
For enterprises daunted by the prospect of AI and investing in a new technology platform, the reality is that AI can leverage already-in-place big data and cloud strategies. This session will explore AI and deep learning use cases that are desig ...Read More

For enterprises daunted by the prospect of AI and investing in a new technology platform, the reality is that AI can leverage already-in-place big data and cloud strategies. This session will explore AI and deep learning use cases that are designed for ROI, and look at how success is being measured and optimized.

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Keywords:
AI and DL Business Track (high level), AI for Business, GTC Silicon Valley 2018 - ID S8983
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Practical Use Cases Of AI and Deep Learning On GPUs In The Cloud For Marketing And Retail
Alexander Tsyplikhin (Data Monsters)
We'll review three practical use cases of applying AI and deep learning in the marketing and retail industries. For each use case, we'll cover business situations, discuss potential approaches, and describe final solutions from both the AI and infr ...Read More
We'll review three practical use cases of applying AI and deep learning in the marketing and retail industries. For each use case, we'll cover business situations, discuss potential approaches, and describe final solutions from both the AI and infrastructural points of view. Attendees will learn about applications of AI and deep learning in marketing and advertising; AI readiness criteria; selecting the right AI and deep learning methods, infrastructure, and GPUs for specific use cases; and avoiding potential risks.  Back
 
Keywords:
AI and DL Business Track (high level), Predictive Analytics for Retail, Consumer Engagement and Personalization, GTC Silicon Valley 2018 - ID S8265
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Earth Observation From Space: Deep Learning based Satellite Image Analysis
Patrick Helber (German Research Center for Artificial Intelligence)
Learn how recent advances in Earth observation are opening up a new exciting area for exploration of satellite image data with deep learning. Focusing on real-world scenarios, we will teach you how to analyze this exciting remote sensing data source ...Read More
Learn how recent advances in Earth observation are opening up a new exciting area for exploration of satellite image data with deep learning. Focusing on real-world scenarios, we will teach you how to analyze this exciting remote sensing data source with deep neural networks. An automated satellite image understanding is of high interest for various research fields and industry sectors such as the insurance, agriculture or investing industry. You will learn how to apply deep neural networks in natural disaster situations and for the classification of land-use, land-cover and building types.  Back
 
Keywords:
AI and DL Business Track (high level), GIS, AI and DL Research, GTC Silicon Valley 2018 - ID S81028
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AI and DL Research
Presentation
Media
Scaling Machine Learning through Decentralization, Quantization, and Structured Sparsity
Dan Alistarh (IST Austria), Ce Zhang (ETH Zurich)
In this session, participants will get a taste of state-of-the-art techniques for scaling Deep Learning on GPU clusters. We present SuperML, a general and efficient communication layer for machine learning, which can scale neural network training to ...Read More
In this session, participants will get a taste of state-of-the-art techniques for scaling Deep Learning on GPU clusters. We present SuperML, a general and efficient communication layer for machine learning, which can scale neural network training to hundreds of GPU nodes. SuperML builds on three main ideas: decentralization, which allows algorithms to converge without a centralized coordinator (parameter server) or all-to-all communication, communication quantization, which significantly speeds up point-to-point messaging, and structured sparsity, by which SuperML induces model updates which only have a limited number of non-zero entries. From the technical perspective, SuperML provides a new implementation of the classic MPI standard, re-designed and re-implemented to provide efficient support for quantization and sparsity. We illustrate the performance characteristics of SuperML on CSCS Piz Daint, Europe's most powerful supercomputer, and on Amazon EC2, improving upon other highly optimized implementations such as CrayMPI and NVIDIA NCCL.  Back
 
Keywords:
AI and DL Research, Accelerated Analytics, HPC and Supercomputing, GTC Silicon Valley 2018 - ID S8668
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Designing Wireless Systems with Deep Learning - An Autoencoder-Based Approach to PHY Layer Design
Ben Hilburn (DeepSig Inc.), Tim O'Shea (DeepSig Inc.)
The field of wireless engineering is on the cusp of a revolution, driven by deep learning, that will define the next paradigm in wireless system design. While wireless communications technology has advanced considerably since its invention in the 189 ...Read More
The field of wireless engineering is on the cusp of a revolution, driven by deep learning, that will define the next paradigm in wireless system design. While wireless communications technology has advanced considerably since its invention in the 1890s, the fundamental design methodology has remained unchanged throughout its history - expert engineers hand-designing radio systems for specific applications. Deep learning enables a new, radically different approach, where systems are learned from wireless channel data. As the world becomes more connected and the Internet of Things becomes a reality, it is difficult to overstate the enormity of the impact to both commercial and military systems. This talk will provide a high-level overview of deep learning applied to wireless communications, discuss the current state of the technology and research, and present a vision for the future of wireless engineering.  Back
 
Keywords:
AI and DL Research, Telecom Industry Solutions, GTC Silicon Valley 2018 - ID S8791
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Deep Learning Applications for Radio Frequency (RF) Data
Adam Thompson (NVIDIA)
We'll discuss applications of deep learning to radio frequency (RF) data including specific signal and digital modulation scheme classification, identification of nefarious activities, and a general overview of the unique challenges and solutions fo ...Read More
We'll discuss applications of deep learning to radio frequency (RF) data including specific signal and digital modulation scheme classification, identification of nefarious activities, and a general overview of the unique challenges and solutions for AI in this domain. With the ubiquity of RF communication signals in our lives, deep learning can be leveraged to ensure accurate signal transmission and safer communities.  Back
 
Keywords:
AI and DL Research, Computational Physics, GTC Silicon Valley 2018 - ID S8826
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Simultaneous Pixel-Localization and Feature Extraction for Multiple Instances in a Scene
Timothy Klein (Arete Associates)
We'll introduce attendees to a new deep learning approach to object-localization. Instead of bounding boxes, our network estimates the center pixel locations for a variable number of targets in a scene while simultaneously extracting a characteristi ...Read More
We'll introduce attendees to a new deep learning approach to object-localization. Instead of bounding boxes, our network estimates the center pixel locations for a variable number of targets in a scene while simultaneously extracting a characteristic feature-set. We'll outline the overall approach and describe the underlying network architecture and training. We'll also present the results of our network as applied to the cars overhead with context dataset and discuss the current and future possibilities of this approach.  Back
 
Keywords:
AI and DL Research, Computer Vision, GTC Silicon Valley 2018 - ID S8191
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Inside NVIDIA GPU Cloud Deep Learning Framework Containers
John Barco (NVIDIA), Christopher Lamb (NVIDIA)
In this technical deep dive, get an in-depth look at the deep learning containers on NVIDIA GPU Cloud (NGC) and learn how they can simplify your AI projects. NVIDIA pre-integrates and optimizes the top deep learning frameworks such as TensorFlow, PyT ...Read More
In this technical deep dive, get an in-depth look at the deep learning containers on NVIDIA GPU Cloud (NGC) and learn how they can simplify your AI projects. NVIDIA pre-integrates and optimizes the top deep learning frameworks such as TensorFlow, PyTorch, and MXNet, and makes them available on NVIDIA GPU Cloud, removing time consuming do-it-yourself software integration. We'll look at the NVIDIA framework optimizations, such as reducing GPU memory overhead, improving multi-GPU scaling, and reducing latency. And we'll talk about the integration of runtimes and drivers in the containers to ensure the correct versions of software are working together for peak performance. You'll leave with an understanding of what make an NVIDIA GPU-optimized deep learning container tick.  Back
 
Keywords:
AI and DL Research, Deep Learning and AI Frameworks, Data Center and Cloud Infrastructure, GTC Silicon Valley 2018 - ID S8497
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Matchbox: Automatic Batching for Dynamic Deep Learning
James Bradbury (Salesforce)
Matchbox is an open source PyTorch-based tool that lets users implement their deep learning models as imperative code that applies to individual data samples, then efficiently train and validate them on batched data using GPUs. By automatically keepi ...Read More
Matchbox is an open source PyTorch-based tool that lets users implement their deep learning models as imperative code that applies to individual data samples, then efficiently train and validate them on batched data using GPUs. By automatically keeping track of batch-level masking and padding and rewriting data-dependent control flow, Matchbox simplifies model code, eliminates a class of implementation bugs, and allows programmers to work directly at a more natural level of abstraction.  Back
 
Keywords:
AI and DL Research, Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S8977
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Tackling the Crowded Radio Frequency Spectrum Using Deep Learning
Krishna Karra (KickView Corporation)
We'll introduce new concepts and algorithms that apply deep learning to radio frequency (RF) data to advance the state of the art in signal processing and digital communications. With the ubiquity of wireless devices, the crowded RF spectrum ...Read More

We'll introduce new concepts and algorithms that apply deep learning to radio frequency (RF) data to advance the state of the art in signal processing and digital communications. With the ubiquity of wireless devices, the crowded RF spectrum poses challenges for cognitive radio and spectral monitoring applications. Furthermore, the RF modality presents unique processing challenges due to the complex-valued data representation, large data rates, and unique temporal structure. We'll present innovative deep learning architectures to address these challenges, which are informed by the latest academic research and our extensive experience building RF processing solutions. We'll also outline various strategies for pre-processing RF data to create feature-rich representations that can significantly improve performance of deep learning approaches in this domain. We'll discuss various use-cases for RF processing engines powered by deep learning that have direct applications to telecommunications, spectral monitoring, and the Internet of Things.

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Keywords:
AI and DL Research, Telecom Industry Solutions, Federal, GTC Silicon Valley 2018 - ID S8267
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Point Cloud Deep Learning
Innfarn Yoo (NVIDIA)
This presentation shows in-depth comparisons of several neural network models for 3D object classification. Object classification from 2D image is studied thoroughly and widely adopted during last few years by following the advances of deep neural ne ...Read More
This presentation shows in-depth comparisons of several neural network models for 3D object classification. Object classification from 2D image is studied thoroughly and widely adopted during last few years by following the advances of deep neural networks. From then, 3D object classification methods are actively studied, and yet not completely mature. Point cloud is most basic format of 3D objects. In this work, we present many neural network models that can be learned from 3D point cloud. It includes directly learning from 3D point cloud, projected 2D pixels, and voxelated volumes. This work uses Princeton ModelNet datasets and ShapeNetCore.v2 dataset, and then provides the comparisons of those neural network models.  Back
 
Keywords:
AI and DL Research, Graphics and AI, Rendering and Ray Tracing, Real-Time Graphics, GTC Silicon Valley 2018 - ID S8453
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New Applications of Deep Learning in Dialogue Generation and Question Answering
Mithun Das Gupta (Microsoft)
The current generation AI systems are mostly moving towards dialogue generation and question answering. Human like conversation and dialogue based interaction has been proposed as the interface for tomorrow, which would obliterate key-boards and trac ...Read More
The current generation AI systems are mostly moving towards dialogue generation and question answering. Human like conversation and dialogue based interaction has been proposed as the interface for tomorrow, which would obliterate key-boards and track-pads from computers as we know them. We present two important current developments in these fields. First we talk about a neural dialogue generation system which can be deployed to engage humans in a multi-turn conversation. Next we talk about a segmented question answering module which can find answers from the web. The combination of these two techniques has the potential to unlock numerous new verticals, such as travel, retail etc. We will talk about the technical details as well as the higher level design choices.  Back
 
Keywords:
AI and DL Research, Speech and Language Processing, Advanced AI Learning Techniques (incl. GANs and NTMs), GTC Silicon Valley 2018 - ID S8151
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Object-Level Deep Reinforcement Learning
William Agnew (University of Washington)
We'll show how deep reinforcement learning can be greatly sped up by separating perception and action, with a reward function specified in terms of objects and their motions, which are supplied by the perceptual system. In the past five years, reinf ...Read More
We'll show how deep reinforcement learning can be greatly sped up by separating perception and action, with a reward function specified in terms of objects and their motions, which are supplied by the perceptual system. In the past five years, reinforcement learners have become vastly more powerful by incorporating deep learning techniques, playing Atari, Mario, Go, and other games with superhuman skill. However, these learners require vast amounts of training data to become skilled. For example, to master Pong, state-of-the-art reinforcement learners require tens of millions of game frames, equivalent to months of play time at human speed. We show that endowing the learner with a minimal perceptual system, capable of detecting and tracking objects, greatly reduces the number of frames needed for learning. This shifts the learning bottleneck from the amount of training data available to computations easily accelerated with GPUs.  Back
 
Keywords:
AI and DL Research, Advanced AI Learning Techniques (incl. GANs and NTMs), GTC Silicon Valley 2018 - ID S8581
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Recent Advances in Neural Machine Translation: Multilingual, Non-Parametric to Unsupervised Neural Machine Translation
Kyunghyun Cho (New York University)
We'll describe the latest advances in neural machine translation from three different perspectives. We'll start with character-level, multilingual neural machine translation, which aims at harnessing positive language transfer among multiple langua ...Read More
We'll describe the latest advances in neural machine translation from three different perspectives. We'll start with character-level, multilingual neural machine translation, which aims at harnessing positive language transfer among multiple languages to improve the translation quality and the robustness of such a multilingual translation model to intra-sentence code-switching and typos. We'll then discuss the recent research on exploiting data beside oft-used parallel corpora. We'll discuss how another modality, such as vision, can be used to enable zero-resource machine translation, and how purely unsupervised neural machine translation can be done by exploiting the similarity between language distributions of two languages. Finally, we'll discuss a recent trend of retrieval-based approaches to deep learning with a specific example of non-parametric neural machine translation.  Back
 
Keywords:
AI and DL Research, Advanced AI Learning Techniques (incl. GANs and NTMs), GTC Silicon Valley 2018 - ID S8609
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Meet Horovod: Uber's Open Source Distributed Deep Learning Framework for TensorFlow
Alexander Sergeev (Uber)
Horovod makes it easy to train a single GPU TensorFlow model on many GPUs; both on a single server and across multiple servers. We'll cover Uber's explorations of distributed deep learning, how to use Horovod, and what kind of performance you ...Read More
Horovod makes it easy to train a single GPU TensorFlow model on many GPUs; both on a single server and across multiple servers. We'll cover Uber's explorations of distributed deep learning, how to use Horovod, and what kind of performance you can get on standard models, such as Inception V3 and ResNet-101. Learn how to speed up training of your TensorFlow model with Horovod.  Back
 
Keywords:
AI and DL Research, Deep Learning and AI Frameworks, HPC and AI, GTC Silicon Valley 2018 - ID S8152
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Instance-Aware Image and Sentence Matching with Selective Multimodal LSTM
Yan Huang (Institute of Automation, Chinese Academy of Sciences)
We'll present a unique framework for cross-modal image and sentence matching; namely selective multimodal long short-term memory (LSTM) that incorporates a new deep learning module as multimodal context-modulated attention network to selectively att ...Read More
We'll present a unique framework for cross-modal image and sentence matching; namely selective multimodal long short-term memory (LSTM) that incorporates a new deep learning module as multimodal context-modulated attention network to selectively attend to pairwise semantic concepts. In detail, effective image and sentence matching depends on measuring their global visual-semantic similarity. Based on the observation that such a global similarity arises from a complex aggregation of multiple local similarities between pairwise instances of image (objects) and sentence (words), we propose a selective multimodal LSTM network (sm-LSTM) for instance-aware image and sentence matching. The sm-LSTM includes a multimodal context-modulated attention scheme at each timestep that can selectively attend to a pair of instances of image and sentence by predicting pairwise instance-aware saliency maps for image and sentence. For selected pairwise instances, their representations are obtained based on the predicted saliency maps, and then compared to measure their local similarity. By similarly measuring multiple local similarities within a few timesteps, the sm-LSTM sequentially aggregate.  Back
 
Keywords:
AI and DL Research, Computer Vision, GTC Silicon Valley 2018 - ID S8281
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(Deep) Learning to Grasp with a Close-Loop DNN Controller
Iuri Frosio (NVIDIA), Mengyuan Yan (Stanford University)
The paradigm for robot programming is changing with the adoption of the deep learning approach in the field of robotics. Instead of hard coding a complex sequence of actions, tasks are acquired by the robot through an active learning procedure. This ...Read More
The paradigm for robot programming is changing with the adoption of the deep learning approach in the field of robotics. Instead of hard coding a complex sequence of actions, tasks are acquired by the robot through an active learning procedure. This introduces new challenges that have to be solved to achieve effective training. We'll show several issues that can be encountered while learning a close-loop DNN controller aimed at a fundamental task like grasping, and their practical solutions. First, we'll illustrate the advantages of training using a simulator, as well as the effects of choosing different learning algorithms in the reinforcement learning and imitation learning domains. We'll then show how separating the control and vision modules in the DNN can simplify and speed up the learning procedure in the simulator, although the learned controller hardly generalizes to the real world environment. Finally, we'll demonstrate how to use domain transfer to train a DNN controller in a simulator that can be effectively employed to control a robot in the real world.  Back
 
Keywords:
AI and DL Research, IoT, Robotics & Drones, Computer Vision, Robotics & Autonomous Machines, GTC Silicon Valley 2018 - ID S8132
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Graduate Fellowship FastForward Talks
Robin Betz (Stanford University), Awni Hannun (Stanford University), Robert Konrad (Stanford University), Deepak Pathak (UC Berkeley), Fereshteh Sadeghi (University of Washington), Abigail See (Stanford University), Anna Shcherbina (Stanford University), Caroline Trippel (Princeton University)
Join a special presentation from our 2017-2018 Graduate Fellowship recipients to learn "what's next" out of the world of research and academia. Sponsored projects involve a variety of technical challenges, including distributed systems for ...Read More
Join a special presentation from our 2017-2018 Graduate Fellowship recipients to learn "what's next" out of the world of research and academia. Sponsored projects involve a variety of technical challenges, including distributed systems for large-scale deep learning; dynamic data structures for massively parallel machine learning; machine learning techniques for biomedical image analysis; visual dynamics; and compilation frameworks for high-performance graphics systems. We believe that these minds lead the future in our industry and we're proud to support the 2016-2017 NVIDIA Graduate Fellows. We'll also announce the 2017-2018 Graduate Fellows at this session. For more information on the NVIDIA Graduate Fellowship program, visit www.nvidia.com/fellowship.  Back
 
Keywords:
AI and DL Research, Virtual Reality and Augmented Reality, Graphics and AI, Computational Biology and Chemistry, Computer Vision, GTC Silicon Valley 2018 - ID S8793
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Deep Learning for Transportation: Fast Estimation of Travel Times Using Historical Routes
Dmitry Kudinov (Esri Inc.)
During this presentation we will review a deep neural network architecture and its training approaches used for producing high volume of estimations of travel times on a road graph with historical routes and traffic. This includes initial and continu ...Read More
During this presentation we will review a deep neural network architecture and its training approaches used for producing high volume of estimations of travel times on a road graph with historical routes and traffic. This includes initial and continuous online training, finding various sources to produce training data, challenges of quality control, and, of course, the invaluable role of GPU's for computation during both training and inference.  Back
 
Keywords:
AI and DL Research, Product & Building Design, Intelligent Video Analytics and Smart Cities, GIS, Autonomous Vehicles, GTC Silicon Valley 2018 - ID S8156
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Block-Sparse Recurrent Neural Networks
Sharan Narang (Baidu USA), Eric Undersander (Baidu USA)
Recurrent neural networks are used in state-of-the-art models in domains such as speech recognition, machine translation, and language modeling. Sparsity is a technique to reduce compute and memory requirements of deep learning models. Sparse RNNs ar ...Read More
Recurrent neural networks are used in state-of-the-art models in domains such as speech recognition, machine translation, and language modeling. Sparsity is a technique to reduce compute and memory requirements of deep learning models. Sparse RNNs are easier to deploy on devices and high-end server processors. Even though sparse operations need less compute and memory relative to their dense counterparts, the speed-up observed by using sparse operations is less than expected on different hardware platforms. To address this issue, we prune blocks of weights in a layer instead of individual weights. Using these techniques, we can create block-sparse RNNs with sparsity ranging from 80% to 90% with a small loss in accuracy. This technique allows us to reduce the model size by 10x. Additionally, we can prune a larger dense network to recover this loss in accuracy while maintaining high block sparsity and reducing the overall parameter count. Our technique works with a variety of block sizes up to 32x32. Block-sparse RNNs eliminate overheads related to data storage and irregular memory accesses while increasing hardware efficiency compared to unstructured sparsity.  Back
 
Keywords:
AI and DL Research, HPC and AI, GTC Silicon Valley 2018 - ID S8924
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Deep Generative Models for Image and Video Creation
Vineeth N Balasubramanian (Indian Institute of Technology (IIT), Hyderabad, India)
We'll focus on recent developments in deep learning-based generative models for image and video creation. The last two to three years have seen an explosive growth in the development of generative adversarial networks, variational autoencoders, and ...Read More
We'll focus on recent developments in deep learning-based generative models for image and video creation. The last two to three years have seen an explosive growth in the development of generative adversarial networks, variational autoencoders, and related autoregressive methods that have been made it possible to automatically generate images and videos, by harnessing the power of GPUs and deep learning libraries. These methods present interesting possibilities in automatic generation of datasets for training machine learning methods, as well as in real-world applications for image and video processing such as morphing, editing, advertising, design, and art. We'll present the technical details of these methods and recent results in various settings.  Back
 
Keywords:
AI and DL Research, Advanced AI Learning Techniques (incl. GANs and NTMs), Video and Image Processing, GTC Silicon Valley 2018 - ID S8784
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Deep Learning Applications in E-Commerce
Krishnendu Chaudhury (Drishti Technologies)
In this talk we will present four applications of deep learning in e-commerce. 1) A deep neural net architecture which has been successfully deployed as a large scale Visual Search and Recommendation system for e-commerce. The deployment has been at ...Read More
In this talk we will present four applications of deep learning in e-commerce. 1) A deep neural net architecture which has been successfully deployed as a large scale Visual Search and Recommendation system for e-commerce. The deployment has been at Flipkart, India's largest e-Commerce vendor, over a catalog of 50M products, supporting 2K queries per second. Our results beat state of the art on the on the Exact Street2Shop dataset. 2) Visual Semantic embedding of e-Commerce products for enhanced searchability and product ranking. 3) Neural Network based click prediction. 4) A novel neural network architecture for demand prediction.  Back
 
Keywords:
AI and DL Research, Deep Learning and AI Frameworks, Consumer Engagement and Personalization, Computer Vision, GTC Silicon Valley 2018 - ID S8684
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Model Architectures and Training Techniques for High-Precision Landmark Localization
Sina Honari (University of Montreal - MILA), Pavlo Molchanov (NVIDIA)
We'll discuss training techniques and deep learning architectures for high-precision landmark localization. In the first part of the session, we'll talk about ReCombinator Networks, which aims at maintaining pixel-level image information ...Read More

We'll discuss training techniques and deep learning architectures for high-precision landmark localization. In the first part of the session, we'll talk about ReCombinator Networks, which aims at maintaining pixel-level image information, for high-accuracy landmark localization. This model combines coarse-to-fine features to first observe global (coarse) image information and then recombines local (fine) information. By using this model, we report SOTA on three facial landmark datasets. This model can be used for other tasks that require pixel-level accuracy (for example, image segmentation, image-to-image translation). In the second part, we'll talk about improving landmark localization in a semi-supervised setting, where less labeled data is provided. Specifically, we consider a scenario where few labeled landmarks are given during training, but lots of weaker labels (for example, face emotions, hand gesture) that are easier to obtain are provided. We'll describe training techniques and model architectures that can leverage weaker labels to improve landmark localization.

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Keywords:
AI and DL Research, Computer Vision, GTC Silicon Valley 2018 - ID S8406
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Training ImageNet In 15 Minutes With ChainerMN: A Scalable Distributed DL Framework
Keisuke Fukuda (Preferred Networks, Inc.)
We''ll present a multi-node distributed deep learning framework called ChainerMN. Even though GPUs are continuously gaining more computation throughput, it is still very time-consuming to train state-of-the-art deep neural network models. For better ...Read More
We''ll present a multi-node distributed deep learning framework called ChainerMN. Even though GPUs are continuously gaining more computation throughput, it is still very time-consuming to train state-of-the-art deep neural network models. For better scalability and productivity, it is paramount to accelerate the training process by using multiple GPUs. To enable high-performance and flexible distributed training, ChainerMN was developed and built on top of Chainer. We''ll first introduce the basic approaches to distributed deep learning and then explain the design choice, basic usage, and implementation details of Chainer and ChainerMN. To demonstrate the scalability and efficiency of ChainerMN, we''ll discuss the remarkable results from training ResNet-50 classification model on ImageNet database using 1024 Tesla P100 GPUs and our in-house cluster, MN-1.    Back
 
Keywords:
AI and DL Research, NVIDIA Inception Program, Deep Learning and AI Frameworks, HPC and AI, GTC Silicon Valley 2018 - ID S8889
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Deep Learning for Computational Science
Yang Juntao (NVIDIA)
We''ll review our study of the use of artificial intelligence to augment various domains of computational science in order to improve time to solution for various HPC problems. We''ll discuss the current state-of-the-art approaches and performance ga ...Read More
We''ll review our study of the use of artificial intelligence to augment various domains of computational science in order to improve time to solution for various HPC problems. We''ll discuss the current state-of-the-art approaches and performance gains where applicable. We''ll also investigate current barriers to adoption and consider possible solutions.  Back
 
Keywords:
AI and DL Research, HPC and AI, GTC Silicon Valley 2018 - ID S8242
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Deep Learning for Driver State Sensing
Lex Fridman (MIT)
We''ll explore how deep learning approaches can be used for perceiving and interpreting the driver''s state and behavior during manual, semi-autonomous, and fully-autonomous driving. We''ll cover how convolutional, recurr ...Read More

We''ll explore how deep learning approaches can be used for perceiving and interpreting the driver''s state and behavior during manual, semi-autonomous, and fully-autonomous driving. We''ll cover how convolutional, recurrent, and generative neural networks can be used for applications of glance classification, face recognition, cognitive load estimation, emotion recognition, drowsiness detection, body pose estimation, natural language processing, and activity recognition in a mixture of audio and video data.

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Keywords:
AI and DL Research, Autonomous Vehicles, Autonomous Driving, GTC Silicon Valley 2018 - ID S8626
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Deep Learning for Recommender Systems
Justin Basilico (Netflix), Yves Raimond (Netflix)
In this talk, we will survey how Deep Learning methods can be applied to personalization and recommendations. We will cover why standard Deep Learning approaches don''t perform better than typical collaborative filtering techniques. Then ...Read More

In this talk, we will survey how Deep Learning methods can be applied to personalization and recommendations. We will cover why standard Deep Learning approaches don''t perform better than typical collaborative filtering techniques. Then we will survey we will go over recently published research at the intersection of Deep Learning and recommender systems, looking at how they integrate new types of data, explore new models, or change the recommendation problem statement. We will also highlight some of the ways that neural networks are used at Netflix and how we can use GPUs to train recommender systems. Finally, we will highlight promising new directions in this space.

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Keywords:
AI and DL Research, Consumer Engagement and Personalization, Deep Learning and AI, GTC Silicon Valley 2018 - ID S81011
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Efficient Communication Library for Large-Scale Deep-Learning
Minsik Cho (IBM Research)
We''ll talk about the challenges in a large-scale distributed, GPU-based deep learning, and propose an efficient communication algorithm to achieve state-of-the-art scalability. In detail, we''ll explain various ways to speed up GPU-based deep learni ...Read More
We''ll talk about the challenges in a large-scale distributed, GPU-based deep learning, and propose an efficient communication algorithm to achieve state-of-the-art scalability. In detail, we''ll explain various ways to speed up GPU-based deep learning, and motivate the large-scale deep-learning in the performance context. Then, we will state that efficient communication is a grand challenge in the large-scale deep-learning, especially with upcoming more powerful GPUs such as Volta architecture Tesla V100. We''ll present the technical details on a proposed communication algorithm along with the supporting data collected with more than 100 GPUs.  Back
 
Keywords:
AI and DL Research, Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S8479
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Deep Learning For Intelligent Multi-Sensor Analytics
Kyle Muchmore (KickView), David Ohm (KickView)
Go beyond working with a single sensor and enter the realm of Intelligent Multi-Sensor Analytics (IMSA). We''ll introduce concepts and methods for using deep learning with multi-sensor, or heterogenous, data. There are many resources and ...Read More

Go beyond working with a single sensor and enter the realm of Intelligent Multi-Sensor Analytics (IMSA). We''ll introduce concepts and methods for using deep learning with multi-sensor, or heterogenous, data. There are many resources and examples available for learning how to leverage deep learning with public imagery datasets. However, few resources exist to demonstrate how to combine and use these techniques to process multi-sensor data. As an example, we''ll introduce some basic methods for using deep learning to process radio frequency (RF) signals and make it a part of your intelligent video analytics solutions. We''ll also introduce methods for adapting existing deep learning frameworks for multiple sensor signal types (for example, RF, acoustic, and radar). We''ll share multiple use cases and examples for leveraging IMSA in smart city, telecommunications, and security applications.

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Keywords:
AI and DL Research, Intelligent Video Analytics and Smart Cities, Autonomous Machines, GTC Silicon Valley 2018 - ID S8260
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Accelerating Scientific Simulation with Generative Adversarial Networks
Luke de Oliveira (Vai Technologies), Benjamin Nachman (Lawrence Berkeley National Laboratory), Michela Paganini (Yale University)
Many scientific and engineering fields increasingly rely on complex and time consuming computational simulation as part of the modern scientific workflow. In many applications, such as High Energy Particle Physics, Cosmology, Geophysics, and others, ...Read More
Many scientific and engineering fields increasingly rely on complex and time consuming computational simulation as part of the modern scientific workflow. In many applications, such as High Energy Particle Physics, Cosmology, Geophysics, and others, simulations are the computational bottleneck for producing and testing results. We introduce the usage of Generative Adversarial Networks (GAN) as a potential tool for speeding up expensive theoretical models and simulations in scientific and engineering applications, ushering in a new era of deep learning-powered scientific discovery. We will show that using a GAN-based High Energy Physics fast simulator on GPUs can provide speedups of up to 100,000x when compared to traditional simulation software, while retaining high levels of precision. Finally, we will discuss modeling and architectural considerations in this domain with the hope of directly empowering scientists and engineers in other fields to experiment with Generative Adversarial Networks in order to speed up simulation across scientific domains.  Back
 
Keywords:
AI and DL Research, Advanced AI Learning Techniques (incl. GANs and NTMs), HPC and AI, GTC Silicon Valley 2018 - ID S81001
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Generate Neural Network Automatically with High Accuracy and High Efficiency
Chao Xue (IBM)
Designing neural network architectures are critical for deep learning applications, but it is so complex and depends on AI experts. We''ll demonstrate how you can learn how to construct neural networks automatically without the human intervention. Th ...Read More
Designing neural network architectures are critical for deep learning applications, but it is so complex and depends on AI experts. We''ll demonstrate how you can learn how to construct neural networks automatically without the human intervention. There are two fundamental limiters to the performance of auto-generated neural networks: accuracy and efficiency, which is caused by searching overhead. We''ll also explore new techniques to make auto-generated neural network methods accurate and efficient, including: end-to-end technology to construct neural network within reinforcement learning, adaptive random search and bayesian optimization framework for different AI domains, such as computer vision, IoT acoustics, NLP and finance; using historical knowledge bases to reduce the searching overhead; and scheduling the execution of searching tasks over multiple NVIDIA GPUs to speed up the searching process. Also, we''ll give both the theoretical analysis and experiment results, which show significant improvement of accuracy and substantial reduction of searching time.  Back
 
Keywords:
AI and DL Research, GTC Silicon Valley 2018 - ID S8234
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The Future of the In-Car Experience
Abdelrahman Mahmoud (Affectiva), Ashutosh Sanan (Affectiva)
As the race to full autonomy accelerates, the in-cab transportation experience is also being redefined. Future vehicles will sense the passengers'' identities and activities, as well as their cognitive and emotional states, to adapt and ...Read More

As the race to full autonomy accelerates, the in-cab transportation experience is also being redefined. Future vehicles will sense the passengers'' identities and activities, as well as their cognitive and emotional states, to adapt and optimize their experience. AI capable of interpreting what we call "people analytics" captured through their facial and vocal expressions, and aspects of the context that surrounds them will power these advances. We''ll give an overview of our Emotion AI solution, and describe how we employ techniques like deep learning-based spatio-temporal modeling. By combining these techniques with a large-scale dataset, we can develop AI capable of redefining the in-cab experience.

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Keywords:
AI and DL Research, NVIDIA Inception Program, Deep Learning and AI Frameworks, Autonomous Vehicles, GTC Silicon Valley 2018 - ID S8758
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Smart City: Deep Learning Model for Car-Pedestrian Interaction
Zoran Kostic (Columbia University)
In this talk we will discuss the work Columbia University, in partnership with NYC government, is using deep learning and GPUs to develop smart city traffic management facilitating support for navigation/movement of multitude of vehicles (including a ...Read More
In this talk we will discuss the work Columbia University, in partnership with NYC government, is using deep learning and GPUs to develop smart city traffic management facilitating support for navigation/movement of multitude of vehicles (including autonomous cars) in dense urban environments with many pedestrians. We will describe our work in real-time tracking of cars and pedestrians, prediction of movement based on historical observations of the intersection, backed by ultra-low latency wireless communications and edge computing nodes.  Back
 
Keywords:
AI and DL Research, Intelligent Video Analytics and Smart Cities, Autonomous Vehicles, GTC Silicon Valley 2018 - ID S8201
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Learning Affinity via Spatial Propagation Networks
Sifei Liu (NVIDIA)
We provide a unified framework on learning affinity in pure data-driven fashion using a linear propagation structure. This is a GPU and deep learning friendly pairwise learning module that does not require solving linear equation, iterative inference ...Read More
We provide a unified framework on learning affinity in pure data-driven fashion using a linear propagation structure. This is a GPU and deep learning friendly pairwise learning module that does not require solving linear equation, iterative inferences or manually defined kernels. Specifically, we develop a three-way connection for the linear propagation model, which formulates a sparse transformation matrix, where all elements can be the output from a deep CNN, but results in a dense affinity matrix that effectively models any task-specific pairwise similarity matrix. The spatial propagation network can be applied to many affinity-related tasks, such as image matting, segmentation and colorization, to name a few. Essentially, the model can learn semantically aware affinity relations for high-level vision tasks due to the powerful learning capability of the deep CNN. We validate the framework on the task of refinement for image segmentation boundaries. Experiments on face parsing and semantic segmentation tasks show that the spatial propagation network provides a general, effective, and efficient solution for generating high-quality segmentation results.  Back
 
Keywords:
AI and DL Research, Computer Vision, Video and Image Processing, GTC Silicon Valley 2018 - ID S8312
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Scaling Deep Learning for Immersive User Interfaces
Joel Hestness (Baidu Research)
Deep learning creates advances following a virtuous recipe: model architecture search, creating large training datasets, and scaling computation. Baidu Research''s Silicon Valley AI Lab develops state-of-the-art conversational user interfaces followi ...Read More
Deep learning creates advances following a virtuous recipe: model architecture search, creating large training datasets, and scaling computation. Baidu Research''s Silicon Valley AI Lab develops state-of-the-art conversational user interfaces following this DL recipe. We research new model architectures and features for speech recognition (Deep Speech 3), speech generation (Deep Voice 3), and natural language processing. To deploy these models in impactful products, we want a deep understanding of how recipe components coordinate to drive accuracy improvements. Through large-scale empirical studies, we find intriguing results about how deep learning is likely to scale: As training set size increases, DL model generalization error and model sizes scale as particular power-law relationships. For a fixed dataset size, as model size grows, training time remains roughly constant -- larger models require fewer steps to converge to the same accuracy. These scaling relationships have significant implications on DL research, practice, and systems. They can assist model debugging, setting accuracy targets, and decisions about dataset growth and future computing system design.  Back
 
Keywords:
AI and DL Research, GTC Silicon Valley 2018 - ID S8899
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Towards Learning to Imagine Videos with Controlled Content
Sergey Tulyakov (Snap Inc.)
We discuss one of the first attempts to teach computers to imagine or generate videos with controlled content using deep learning generative modeling techniques. To this end, we assume visual information in a natural video can be decomposed into two ...Read More
We discuss one of the first attempts to teach computers to imagine or generate videos with controlled content using deep learning generative modeling techniques. To this end, we assume visual information in a natural video can be decomposed into two major components: content and motion. While content encodes the objects present in the video, motion encodes the object dynamics. Based on this prior, we propose the motion and content decomposed generative adversarial network (MoCoGAN) framework for video generation. The proposed framework generates a video clip by sequentially mapping random noise vectors to video frames. We divide a random noise vector into content and motion parts. By controlling these parts we generate both the content of the video and the action that is being performed. We perform quantitative and qualitative analysis on several video datasets, including artificial shape motion, facial expression, and tai-chi videos.  Back
 
Keywords:
AI and DL Research, Advanced AI Learning Techniques (incl. GANs and NTMs), Computer Vision, GTC Silicon Valley 2018 - ID S8477
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Quick and Easy DL Workflow Proof of Concept
Alec Gunny (NVIDIA), Kenneth Hester (NVIDIA), Jeffrey Weiss (NVIDIA)
Spin up a deep learning (DL) proof-of-concept on a budget. We'll walk you through a DL workflow in the cloud leveraging DIGITS, then download a trained model, and run inference on a Jetson TX2. This session considers multiple options such as Nimbix, ...Read More
Spin up a deep learning (DL) proof-of-concept on a budget. We'll walk you through a DL workflow in the cloud leveraging DIGITS, then download a trained model, and run inference on a Jetson TX2. This session considers multiple options such as Nimbix, AMI, and NGC on Tesla P100, Tesla V100, and NVIDIA DGX-1 servers. This tutorial will be a combination of lecture, live demos, and detailed instructions.  Back
 
Keywords:
AI and DL Research, Accelerated Analytics, GTC Silicon Valley 2018 - ID S8286
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Deep Learning Applications in Text and Graphics at NVIDIA
Bryan Catanzaro (NVIDIA)
At NVIDIA, we're busy applying deep learning to diverse problems, and this talk will give an overview of a few of these applications. We'll discuss our resume matching system, which helps match candidates to job openings at NVIDIA, as well as an op ...Read More
At NVIDIA, we're busy applying deep learning to diverse problems, and this talk will give an overview of a few of these applications. We'll discuss our resume matching system, which helps match candidates to job openings at NVIDIA, as well as an open-source sentiment analysis project trained on unsupervised text that is improving our marketing capabilities. We'll discuss a blind image quality metric that we're using to lower the cost of raytracing photorealistic graphics, and a generative model that we've built to create realistic graphics from simplistic sketches.  Back
 
Keywords:
AI and DL Research, GTC Silicon Valley 2018 - ID S8672
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Unleashing the Imagination: Combining systems+software innovation with GPUs to create new capabilities
Hillery Hunter (IBM)
AI is one of the most rapidly-evolving areas of computer science today and datascientists are constantly pushing the boundaries of the possible -- wanting to explore new data types, new algorithms, and diverse and heterogenous models. In this talk we ...Read More
AI is one of the most rapidly-evolving areas of computer science today and datascientists are constantly pushing the boundaries of the possible -- wanting to explore new data types, new algorithms, and diverse and heterogenous models. In this talk we'll explore two key productivity factors for datascience -- first, speed and the ability to explore many models and sets of data quickly; and second, ability to explore broad types of models, incorporating both machine learning and deep learning. We will talk about results of 40x and 50x productivity through system+software co-design and novel algorithms which leverage Power Systems and GPUs for both deep learning and key areas of classical machine learning. System+software co-design and co-optimization can result in dramatic efficiency improvements, enable creation of large models, exploration of large datasets, and realize productivity gains for datascientists, freeing them up to focus on the fundamental science of deep and machine learning -- gaining accuracy, functionality, and generalizability of their models.  Back
 
Keywords:
AI and DL Research, GTC Silicon Valley 2018 - ID S81025
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Adaptive Ray Tracing Rendering Powered by Deep Learning
Andrew Tao (NVIDIA), Carsten Waechter (NVIDIA)
This session will present a proof of concept where a deep neural network was trained with pairs of Iray ray traced images (one arbitrary ray tracing iteration number and one fully converged image) and theirs structural similarity index (SSIM). Origin ...Read More
This session will present a proof of concept where a deep neural network was trained with pairs of Iray ray traced images (one arbitrary ray tracing iteration number and one fully converged image) and theirs structural similarity index (SSIM). Originally thought as a method for measuring the similarity between two images, SSIM index can also be viewed as a quality measure versus a reference image or, in our case, as a ray tracing rendering progress. The DNN can now from any render iteration of arbitrary scene infer a rendering progress estimator but also provides heat map pictures of the scenes that can be used for adaptive rendering, focusing ray tracing engine power on appropriate zones.  Back
 
Keywords:
AI and DL Research, Graphics and AI, Rendering and Ray Tracing, GTC Silicon Valley 2018 - ID S8788
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Deep Learning for Dialogue Systems
Yun-Nung (Vivian) Chen (National Taiwan University)
Learn how to apply deep learning technologies for building robust and scalable dialogue systems with deeper understanding of the classic pipelines as well as detailed knowledge on the benchmark of the models of the prior work. We'll start with an ov ...Read More
Learn how to apply deep learning technologies for building robust and scalable dialogue systems with deeper understanding of the classic pipelines as well as detailed knowledge on the benchmark of the models of the prior work. We'll start with an overview of the dialogue research and allow the audience to dive deep into the state-of-the-art work about neural-based language understanding, dialogue management, and language generation towards end-to-end neural dialogue systems.  Back
 
Keywords:
AI and DL Research, Speech and Language Processing, GTC Silicon Valley 2018 - ID S8542
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IamNN: Iterative and Adaptive Mobile Neural Network for Efficient Image Classification
Pavlo Molchanov (NVIDIA)
Deep residual networks (ResNets) made a recent breakthrough in deep learning. The core idea of ResNets is to have shortcut connections between layers that allow the network to be much deeper while still being easy to optimize avoiding vanishing ...Read More

Deep residual networks (ResNets) made a recent breakthrough in deep learning. The core idea of ResNets is to have shortcut connections between layers that allow the network to be much deeper while still being easy to optimize avoiding vanishing gradients. These shortcut connections have interesting properties that make ResNets behave differently from other typical network architectures. In this talk we will use these properties to design a network based on a ResNet but with parameter sharing and adaptive computation time, we call it IamNN. The resulting network is much smaller than the original network and can adapt the computational cost to the complexity of the input image. During this talk we will provide an overview of ways to design compact networks, give an overview of ResNets properties and discuss how they can be used to design compact dense network with only 5M parameters for ImageNet classification.

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Keywords:
AI and DL Research, GTC Silicon Valley 2018 - ID S8456
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Multimodal Memory Modelling for Video Captioning
Yan Huang (Institute of Automation, Chinese Academy of Sciences)
This talk presents a novel framework named multimodal memory model for video captioning, which builds a visual and textual shared memory to model the long-term visual-textual dependency and further guide visual attention on described visual targets t ...Read More
This talk presents a novel framework named multimodal memory model for video captioning, which builds a visual and textual shared memory to model the long-term visual-textual dependency and further guide visual attention on described visual targets to solve visual-textual alignments. Video captioning which automatically translates video clips into natural language sentences is a very important task in computer vision. By virtue of recent deep learning technologies, video captioning has made great progress. However, learning an effective mapping from the visual sequence space to the language space is still a challenging problem due to the long-term multimodal dependency modelling and semantic misalignment. Inspired by the facts that memory modelling poses potential advantages to long-term sequential problems and working memory is the key factor of visual attention, the proposed model attaches an external memory to store and retrieve both visual and textual contents by interacting with video and sentence with multiple read and write operations.  Back
 
Keywords:
AI and DL Research, Computer Vision, Video and Image Processing, GTC Silicon Valley 2018 - ID S8311
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Investigating Data Augmentation Strategies for Advancing Deep Learning Training
Winston Hsu (National Taiwan University)
We saw the huge success of the deep learning paradigm and the superhuman capability in numerous benchmarks in image, video, audio, or text. However, it poses huge challenges as adopting the methods in industrial applications (mainly due to the lack o ...Read More
We saw the huge success of the deep learning paradigm and the superhuman capability in numerous benchmarks in image, video, audio, or text. However, it poses huge challenges as adopting the methods in industrial applications (mainly due to the lack of quality tracking data) as the neural networks consume enormous parameters and require relatively huge quality training data. We'll aim for investigating the "data augmentation" strategies increasing quality training data for robust inference across different learning problems mainly in image, video, 3D, and IoT data streams. We'll first quantify the importance of training data for deep neural networks then review numerous strategies, such as crawling from the web, utilizing generative models, 3D computer graphics, augmented reality, engagement in social media, gaming, etc. We'll compare the effectiveness among the diverse strategies. As generally taking the data from other domains, we also need to deal with the cross-domain learning problem. We'll provide detailed insights from our recent work published in top conferences (e.g., CVPR, ICCV, AAAI, etc.) and those cases in industrial applications.  Back
 
Keywords:
AI and DL Research, Advanced AI Learning Techniques (incl. GANs and NTMs), GTC Silicon Valley 2018 - ID S8391
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Tensor Layers for Compression of Deep Learning Networks
Cristopher Cecka (NVIDIA)
We'll review recent efforts to compress fully connected layers in machine learning via tensor networks, including the Tensor Train format, the Tensor Contraction Layer, the Tensor Regression Layer, and a Tensor Ring decomposition. These decompositio ...Read More
We'll review recent efforts to compress fully connected layers in machine learning via tensor networks, including the Tensor Train format, the Tensor Contraction Layer, the Tensor Regression Layer, and a Tensor Ring decomposition. These decompositions, in supplementing or replacing fully connected layers, are shown to dramatically reduce the number of parameters required by the network without resorting to sparsity and without loss in error. We've shown 55-80 percent compression of the entire network with less than one percent loss in accuracy. These Tensor layers can be used in end-to-end training, fine-tuning, and transfer-learning by initializing the decomposition with a pre-trained fully connected layer. Furthermore, because the forward and backward passes of the network rely on dense Tensor contractions, we show that these methods retain high computational intensity and can be efficiently evaluated on GPUs.  Back
 
Keywords:
AI and DL Research, Algorithms and Numerical Techniques, HPC and AI, GTC Silicon Valley 2018 - ID S8807
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Accelerating Cancer Research with Deep Learning
Fernanda Foertter (NVIDIA)
The Department of Energy (DOE) entered into a partnership with the National Cancer Institute (NCI) of the National Institutes of Health (NIH) to accelerate cancer research. This "Cancer Moonshot" aims to tackle three main objectives: better ...Read More
The Department of Energy (DOE) entered into a partnership with the National Cancer Institute (NCI) of the National Institutes of Health (NIH) to accelerate cancer research. This "Cancer Moonshot" aims to tackle three main objectives: better understand the mechanisms of cancer, use large amounts of diverse medical data for predictive models, and enable precision medicine by providing guidance for treatment to individual patients. Leveraging the compute expertise of DOE in high performance computing (HPC) and new methods for deep learning in artificial intelligence, this HPC+AI approach aims to create a single scalable deep neural network code called CANDLE (CANcer Distributed Learning Environment) that will be used to address all three challenges. This talk aims to give an overview of the project and highlight how GPU accelerated systems in the DOE ecosystem, Summit and Sierra, have contributed to the project.  Back
 
Keywords:
AI and DL Research, HPC and AI, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S81033
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GPU Performance Testing and PowerAI on IBM Cloud (Presented by IBM Cloud)
Alex Hudak (IBM), Brian Wan (IBM)
In this session, you will learn about the latest IBM PowerAI solution, IBM Cloud GPU offerings and see a price-performance comparison, with supporting data, on the number of CPUs required to optimize GPU performance. We've also aggregated extensive ...Read More
In this session, you will learn about the latest IBM PowerAI solution, IBM Cloud GPU offerings and see a price-performance comparison, with supporting data, on the number of CPUs required to optimize GPU performance. We've also aggregated extensive test data to determine general best practices such as half-precision deep learning advantages on the Tesla V100 and the implications of neural-network model variable distribution and gradient aggregation techniques on your performance results. Join us to see why NVIDIA GPUs on IBM Cloud offer superior results.  Back
 
Keywords:
AI and DL Research, Accelerated Analytics, GTC Silicon Valley 2018 - ID S81013
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AI for Gaming
Presentation
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Deep Learning for Locomotion Animation
Gavriel State (NVIDIA)
We''ll examine tools and technologies that NVIDIA''s GameWorks team is building to leverage the power of deep learning for content creation, demonstrating recent research in ways that neural networks can be used to generate realistic looking human an ...Read More
We''ll examine tools and technologies that NVIDIA''s GameWorks team is building to leverage the power of deep learning for content creation, demonstrating recent research in ways that neural networks can be used to generate realistic looking human animation. We''ll talk about how to apply GPUs for high-performance runtime inferencing of these networks for use in games or real-time VFX scenarios.  Back
 
Keywords:
AI for Gaming, GTC Silicon Valley 2018 - ID S8743
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Production-Level Performance Capture Using Deep Convolutional Neural Networks
Antti Herva (Remedy Games)
We''ll present a machine learning solution that enables cost-efficient creation of large amounts of high-quality facial animation for digital doubles in games. Remedy Entertainment, NVIDIA, and the University of Southern California recently published ...Read More
We''ll present a machine learning solution that enables cost-efficient creation of large amounts of high-quality facial animation for digital doubles in games. Remedy Entertainment, NVIDIA, and the University of Southern California recently published "Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks" as part of the Symposium on Computer Animation. We''ll cover topics including recording a facial animation dataset for an actor, setting up a deep learning project, preprocessing the data, training a deep convolutional neural network, and evaluating the results. We''ll also present a summary of the findings and discuss potential future work.  Back
 
Keywords:
AI for Gaming, Graphics and AI, GTC Silicon Valley 2018 - ID S8734
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Democratizing Deep Learning with Unity ML-Agents
Arthur Juliani (Unity Technologies)
Unity ML-Agents is an open source AI toolkit that enables machine learning developers and researchers to train agents in realistic, complex scenarios with decreased technical barriers. ML-Agents offers a flexible way to use the Unity Editor and Engin ...Read More
Unity ML-Agents is an open source AI toolkit that enables machine learning developers and researchers to train agents in realistic, complex scenarios with decreased technical barriers. ML-Agents offers a flexible way to use the Unity Editor and Engine to develop and test new AI algorithms quickly and efficiently across games, robotics, and beyond. We''ll walk you through new learning methods that are bundled with the latest version of Unity ML-Agents. This includes, (1) Imitation Learning: Train agents to mimic human behavior. (2) Multi-agent Reinforcement Learning: Train multiple agents together to fulfill cooperative, competitive, and general tasks. We''ll showcase these new learning methods in some interesting training scenarios with real game examples.  Back
 
Keywords:
AI for Gaming, Graphics and AI, GTC Silicon Valley 2018 - ID S8740
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Large-Scale Platform for Multi-Player Online Battle Arena (MOBA) Game AI
Qiang Fu (Tencent AI Lab), Bin Wu (Tencent AI Lab)
We have been developing Multi-Player Online Battle Arena (MOBA) Game AI to push AI research boundaries in pursuit of general AI. One of the key challenges in 5v5 MOBA AI development is how to process massive game replays and feed the data to model tr ...Read More
We have been developing Multi-Player Online Battle Arena (MOBA) Game AI to push AI research boundaries in pursuit of general AI. One of the key challenges in 5v5 MOBA AI development is how to process massive game replays and feed the data to model training in an efficient and reliable manner. To address this, we have built a large-scale game AI platform where millions of CPUs and thousands of GPUs are efficiently scheduled. Powered by our game AI platform and scheduling schemes, our MOBA AI is capable of learning upon billions of high-quality user replay samples per day using both deep learning and self-play.  Back
 
Keywords:
AI for Gaming, Data Center and Cloud Infrastructure, AI and DL Research, GTC Silicon Valley 2018 - ID S8922
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AI in Healthcare
Presentation
Media
Automated Segmentation of Suspicious Breast Masses from Ultrasound Images
Viksit Kumar (Mayo Clinic College of Medicine and Science)
Learn how to apply deep learning for detecting and segmenting suspicious breast masses from ultrasound images. Ultrasound images are challenging to work with due to the lack of standardization of image formation. Learn the appropriate data augme ...Read More

Learn how to apply deep learning for detecting and segmenting suspicious breast masses from ultrasound images. Ultrasound images are challenging to work with due to the lack of standardization of image formation. Learn the appropriate data augmentation techniques, which do not violate the physics of ultrasound imaging. Explore the possibilities of using raw ultrasound data to increase performance. Ultrasound images collected from two different commercial machines are used to train an algorithm to segment suspicious breast with a mean dice coefficient of 0.82. The algorithm is shown to perform at par with conventional seeded algorithm. However, a drastic reduction in computation time is observed enabling real-time segmentation and detection of breast masses.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8525
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Ultrasound Medical Imaging in the GPU Era
Marcin Lewandowski (us4us Ltd.)
Fast, inexpensive and safe, ultrasound imaging is the modality of choice for the first level of medical diagnostics. The emerging solutions of portable and hand-held 2/3D scanners, advanced imaging algorithms, and deep learning promise further d ...Read More

Fast, inexpensive and safe, ultrasound imaging is the modality of choice for the first level of medical diagnostics. The emerging solutions of portable and hand-held 2/3D scanners, advanced imaging algorithms, and deep learning promise further democratization of this technology. During the session, we will present an overview of ultrasound imaging techniques in medical diagnostics, explore the future of ultrasound imaging enabled by GPU processing, as well as set out the path to the conception of a portable 3D scanner. We will also demonstrate our hardware developments in ultrasound platforms with GPU-based processing. Having started with one large research scanner, we have begun our migration towards more commercially-viable solutions with a small hand-held unit built on the mobile GPU NVidia Tegra X1.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8421
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Computational Pathology at Scale: Changing Clinical Practice One Petabyte at a Time
Thomas Fuchs (Memorial Sloan Kettering Cancer Center)
How can we train medical deep learning models at a petabyte scale and how can these models impact clinical practice? We will discuss possible answers to these questions in the field of Computational Pathology. Pathology is in the midst of a revo ...Read More

How can we train medical deep learning models at a petabyte scale and how can these models impact clinical practice? We will discuss possible answers to these questions in the field of Computational Pathology. Pathology is in the midst of a revolution from a qualitative to a quantitative discipline. This transformation is fundamentally driven by machine learning in general and computer vision and deep learning in particular. With the help of PAIGE.AI we are building a clinical-grade AI at Memorial Sloan Kettering Cancer Center. The models are trained based on petabytes of image and clinical data on top of the largest DGX-1 V100 cluster in pathology. The goal is not only to automated cumbersome and repetitive tasks, but to impact diagnosis and treatment decisions in the clinic. This talk will focus on our recent advances in deep learning for tumor detection and segmentation, on how we train these high capacity models with annotations collected from pathologists, and how the resulting systems are implemented in the clinic.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8960
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R&D on Medical Imaging
Mei Han (Ping An Technology, US Research Lab)
In this talk I will describe the research and development work on medical imaging, done at PingAn Technology and Google Cloud, covering five different tasks. I'll present the technical details of the deep learning approaches we have develope ...Read More

In this talk I will describe the research and development work on medical imaging, done at PingAn Technology and Google Cloud, covering five different tasks. I'll present the technical details of the deep learning approaches we have developed, and share with the audiences the research direction and scope in the medical fields at PingAn technology and PingAn USA Lab.

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Keywords:
AI in Healthcare, Computer Vision, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8930
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Not Just a Black Box: Interpretable Deep Learning for Genomics and Beyond
Avanti Shrikumar (Stanford University)
Deep learning models give state-of-the-art results on diverse problems, but their lack of interpretability is a major problem. Consider a model trained to predict which DNA mutations cause disease: if the model performs well, it has likely ident ...Read More

Deep learning models give state-of-the-art results on diverse problems, but their lack of interpretability is a major problem. Consider a model trained to predict which DNA mutations cause disease: if the model performs well, it has likely identified patterns that biologists would like to understand. However, this is difficult if the model is a black box. We present algorithms that provide detailed explanations for individual predictions made by a deep learning model and discover recurring patterns across the entire dataset. Our algorithms address significant limitations of existing interpretability methods. We show examples from genomics where the use of deep learning in conjunction with our interpretability algorithms leads to novel biological insights.

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Keywords:
AI in Healthcare, Bioinformatics & Genomics, GTC Silicon Valley 2018 - ID S8907
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A Deep Learning-Based Intelligent Reference Library for Diagnostic Decision Support in Lung Cancer Screening
Daniel Golden (Arterys), Sean Sall (Arterys)
Radiological diagnosis and interpretation should not take place in a vacuum -- but today, it does. One of the greatest challenges the radiologist faces when interpreting studies is understanding the individual patient in the context of the milli ...Read More

Radiological diagnosis and interpretation should not take place in a vacuum -- but today, it does. One of the greatest challenges the radiologist faces when interpreting studies is understanding the individual patient in the context of the millions of patients who have come previously. Without access to historical data, radiologists must make clinical decisions based only on their memory of recent cases and literature. Arterys is working to empower the radiologist with an intelligent lung nodule reference library that automatically retrieves historical cases that are relevant to the current case. The intelligent lung nodule reference library is built on top of our state-of-the-art deep learning-based lung nodule detection, segmentation and characterization system.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8507
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A Component-Based AI Engine Platform for Medical Workflow
Xu Chen (Winning Health)
As deep learning techniques have been applied to the field of healthcare, more and more AI-based medical systems continue to come forth, which are accompanied by new heterogeneity, complexity and security risks. In the real-world we've seen ...Read More

As deep learning techniques have been applied to the field of healthcare, more and more AI-based medical systems continue to come forth, which are accompanied by new heterogeneity, complexity and security risks. In the real-world we've seen this sort of situation lead to demand constraints, hindering AI applications development in China's hospitals. First, we'll share our experience in building a unified GPU accelerated AI engine system to feed component-based functionality into the existing workflow of clinical routine and medical imaging. Then, we'll demonstrate in a pipeline of integrating the different types of AI applications (detecting lung cancer, predicting childhood respiratory disease and estimating bone age) as microservice to medical station, CDSS, PACS and HIS system to support medical decision-making of local clinicians. On this basis, we'll describe the purpose of establishing an open and unified, standardized, legal cooperation framework to help AI participants to enter the market in China to build collaborative ecology.

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Keywords:
AI in Healthcare, Product & Building Design, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8895
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Deep Learning Improves Neuroimaging: Faster, Safer, Cheaper and Smarter
Enhao Gong (Stanford University, Subtle Medical), Greg Zaharchuk (Stanford University)
We will introduce deep learning applications in clinical neuroimaging (using MRI, CT, PET, etc.) and recent breakthrough results from Stanford and Subtle Medical. Perspectives and feedbacks of applying AI technologies in neuroimaging are shared, ...Read More

We will introduce deep learning applications in clinical neuroimaging (using MRI, CT, PET, etc.) and recent breakthrough results from Stanford and Subtle Medical. Perspectives and feedbacks of applying AI technologies in neuroimaging are shared, from expert radiologists and deep learning experts. How Deep Learning/AI is changing clinical neuroimaging practice * How will deep learning be applied in radiology workflow right now and in the future * Practical concerns and perspectives from radiologists How Deep Learning assists smarter neuroimaging decision making * Multi-scale 3D network enables lesion outcome prediction for stroke * More accurate lesion segmentation in neuroimaging How Deep Learning enables safer and cheaper neuroimaging screening * Deep Learning and GAN enables >95% reduction in radiation for functional medical imaging * Deep Learning enables 90% reduction in chemical (Gadolinium) contrast agent usage in contrast enhanced MRI How Deep Learning accelerate neuroimaging * Further acceleration and improved MRI reconstruction using deep learning * Deep Generative Adversarial Network for Compressed Sensing

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8647
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Deep Learning and Use of GPUs in Mammography
Hsi-Ming Chang (CureMetrix)
Discuss the difficulties in digital mammography, and the computational challenges we encountered while adapting deep learning algorithms, including GAN, to digital mammography. Learn how we address those computational issues, and get the informa ...Read More

Discuss the difficulties in digital mammography, and the computational challenges we encountered while adapting deep learning algorithms, including GAN, to digital mammography. Learn how we address those computational issues, and get the information of our benchmarking results using both consumer and enterprise grade GPUs.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8482
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From Promising Algorithms to Clinical Practice: Next Generation of Challenges
Wiro Niessen (Medical Image Computing and Computer Assisted Interventions (MICCAI))
There is large promise in machine learning methods for the automated analysis of medical imaging data for supporting disease detection, diagnosis and prognosis. These examples include the extraction of quantitative imaging biomarkers that are re ...Read More

There is large promise in machine learning methods for the automated analysis of medical imaging data for supporting disease detection, diagnosis and prognosis. These examples include the extraction of quantitative imaging biomarkers that are related to presence and stage of disease, radiomics approaches for tumor classification and therapy selection, and deep learning methods for directly linking imaging data to clinically relevant outcomes. However, the translation of such approaches requires methods for objective validation in clinically realistic settings or clinical practice. In this talk, I will discuss the role of next generation challenges for this domain.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8992
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Frontiers of AI in Medical Imaging: Overcoming Current Challenges and Moving Beyond Classification
Imon Banerjee (Stanford University), Daniel Rubin (Stanford University)
Learn about the key types of clinical use cases for AI methods in medical imaging beyond simple image classification that will ultimately improve medical practice, as well as the critical challenges and progress in applying AI to these applicati ...Read More

Learn about the key types of clinical use cases for AI methods in medical imaging beyond simple image classification that will ultimately improve medical practice, as well as the critical challenges and progress in applying AI to these applications. We''ll first describe the types of medical imaging and the key clinical applications for deep learning for improving image interpretation. Next, we''ll describe recent developments of word-embedding methods to leverage narrative radiology reports associated with images to generate automatically rich labels for training deep learning models and a recent AI project that pushes beyond image classification and tackles the challenging problem of clinical prediction. We''ll also describe emerging methods to leverage multi-institutional data for creating AI models that do not require data sharing and recent innovative approaches of providing explanation about AI model predictions to improve clinician acceptance.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8295
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Medical Imaging with TensorFlow
Josh Gordon (Google)
Dive in to recent work in medical imaging, where TensorFlow is used to spot cancerous cells in gigapixel images, and helps physicians to diagnose disease. During this talk, we''ll introduce concepts in Deep Learning, and show concrete co ...Read More

Dive in to recent work in medical imaging, where TensorFlow is used to spot cancerous cells in gigapixel images, and helps physicians to diagnose disease. During this talk, we''ll introduce concepts in Deep Learning, and show concrete code examples you can use to train your own models. In addition to the technology, we''ll cover problem solving process of thoughtfully applying it to solve a meaningful problem. We''ll close with our favorite educational resources you can use to learn more about TensorFlow.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8919
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Highly Accurate Brain Stroke Diagnosis System and Generative Stroke Lesion Model
Junghwan Cho (CAIDE Systems, Inc)
Learn CAIDE Systems'' unique diagnosis system with highly accurate prediction and delineation of brain stroke lesion. We''ll present how we increase sensitivity in medical diagnosis system and how we develop a state-of-the-art ge ...Read More

Learn CAIDE Systems'' unique diagnosis system with highly accurate prediction and delineation of brain stroke lesion. We''ll present how we increase sensitivity in medical diagnosis system and how we develop a state-of-the-art generative deep learning model for acquiring segmented stroke lesion CT images, and demonstrate our market-ready product: a diagnostic tool as well as a medical deep learning platform. We trained our diagnostic system using CT image data from thousands of patients with brain stroke and tested to see commercial feasibility of use for hospitals and mobile ambulances.  

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8428
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Deep Learning for Shallow Sequencing
Yonatan Israeli (NVIDIA)
The NVIDIA Genomics Group has developed a deep learning platform to transform noisy, low-quality DNA sequencing data into clean, high-quality data. Hundreds of DNA sequencing protocols are used to profile phenomena such as protein-DNA binding an ...Read More

The NVIDIA Genomics Group has developed a deep learning platform to transform noisy, low-quality DNA sequencing data into clean, high-quality data. Hundreds of DNA sequencing protocols are used to profile phenomena such as protein-DNA binding and DNA accessibility. For example, the ATAC-seq protocol identifies open genomic sites by sequencing open DNA fragments; genome-wide fragment counts provide a profile of DNA accessibility. Recent advances enable profiling from smaller patient samples than previously possible. To reduce sequencing cost, we developed a convolutional neural network that denoises data from a small number of DNA fragments, making the data suitable for various downstream tasks. Our platform aims to accelerate adoption of DNA sequencers by minimizing data requirements.

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Keywords:
AI in Healthcare, Bioinformatics & Genomics, GTC Silicon Valley 2018 - ID S8602
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The Early Detection of Pancreatic Cancer Using Deep Learning: Preliminary Observations
Elliot Fishman (Johns Hopkins Hospital)
This talk will present the challenges and opportunities in developing a deep learning program for use in medical imaging. It will present a hands on approach to the challenges that need to be overcome and the need for a multidisciplinary approac ...Read More

This talk will present the challenges and opportunities in developing a deep learning program for use in medical imaging. It will present a hands on approach to the challenges that need to be overcome and the need for a multidisciplinary approach to help define the problems and potential solutions. The role of highly curated data for training the algorithms and the challenges in creating such datasets is addressed. The annotation of data becomes a key point in training and testing the algorithms. The role of experts in computer vision, and radiology will be addressed and how this project can prove to be a roadmap for others planning collaborative efforts will be addressed Finally I will discuss the early results of the Felix project whose goal is nothing short of the early detection of pancreatic cancer to help improve detection and ultimately improve patient outcomes.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S81004
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GE's Evolution from HPC to AI in Healthcare
Keith Bigelow (GE Healthcare Waukesha), Erik Steen (GE Healthcare)
For more than a decade, GE has partnered with Nvidia in Healthcare to power our most advanced modality equipment, from CT to Ultrasound. Part 1 of this session will offer an introduction to the deep learning efforts at GEHC, the platform we' ...Read More

For more than a decade, GE has partnered with Nvidia in Healthcare to power our most advanced modality equipment, from CT to Ultrasound. Part 1 of this session will offer an introduction to the deep learning efforts at GEHC, the platform we're building on top of NGC to accelerate new algorithm development, and then a deep dive into a case study of the evolution of our cardiovascular ultrasound scanner and the underlying extensible software stack. It will contain 3 main parts as follows: (a) Cardiovascular ultrasound imaging from a user perspective. Which problems we need to solve for our customers. Impact of Cardiovascular disease in a global perspective (b) An introduction to the Vivid E95 and the cSound platform , GPU based real time image reconstruction & visualization. How GPU performance can be translated to customer value and outcomes and how this has evolved the platform during the last 2 ½ years. (c) Role of deep learning in cardiovascular ultrasound imaging, how we are integrating deep learning inference into our imaging system and preliminary results from automatic cardiac view detection.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8849
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Deep Learning Brings Disruptive Changes to Ophthalmology
Aaron Lee (University of Washington)
Hear about how GPU technology is disrupting the way your eye doctor works and how ophthalmic research is performed today. The rise of Electronic Medical Records in medicine has created mountains of Big Data particularly in ophthalmology where ma ...Read More

Hear about how GPU technology is disrupting the way your eye doctor works and how ophthalmic research is performed today. The rise of Electronic Medical Records in medicine has created mountains of Big Data particularly in ophthalmology where many discrete quantitative clinical elements like visual acuity can be tied to rich imaging datasets. In this session, we will explore the transformative nature that GPU acceleration has played in accelerating clinical research and show real-life examples of deep learning applications to ophthalmology in creating new steps forward in automated diagnoses, image segmentation, and computer aided diagnoses.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8866
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AI DR Screening for Chronic Diseases
Emma Xu (Airdoc)
Diabetic retinopathy, also known as diabetic eye disease, is a major complication of diabetes, which damage occurs to the retina due to diabetes mellitus and is a leading cause of blindness. AirDoc's product Dirctor, Emma Xu and Professor Yo ...Read More

Diabetic retinopathy, also known as diabetic eye disease, is a major complication of diabetes, which damage occurs to the retina due to diabetes mellitus and is a leading cause of blindness. AirDoc's product Dirctor, Emma Xu and Professor You Li of Shanghai Changzheng Hospital, will share how AirDoc, the leading Intelligent Medical startup in China, leverages Nvidia's GPU and Deep Learning to improve the DR diagnose with Automatic left/right eye recognition, Automatic detection of the location and numbers, Automatic DR staging, Fast recognition speed, Patient Information Management for real-time screening statistics and usage management.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8940
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Accelerated Analytics
Presentation
Media
Building an Enterprise Machine Learning Center of Excellence
Zachary Hanif (Capital One)
Algorithmic advancements and new research capabilities frequently overshadow the infrastructure that enables that research and serves it to customers in production applications. Having a solid infrastructure for real world machine learning often ends ...Read More
Algorithmic advancements and new research capabilities frequently overshadow the infrastructure that enables that research and serves it to customers in production applications. Having a solid infrastructure for real world machine learning often ends up being the biggest determinant of success and is an exciting area of research and engineering in its own right. These environments are what allow brilliant algorithms to deliver value at scale. We'll detail how Capital One has designed its GPU computing environment to accelerate machine learning efforts and outline the services used, the framework to leverage those services, and the engineering practices used to develop and deploy well-governed, accurate models to high-volume production environments. Beyond production deployments, we'll discuss how this infrastructure performs large-scale testing of models and frameworks to explore the interactions of deep learning tools like MXNet and TensorFlow. We'll also discuss the practices that enabled Capital One to hire a high-performing team in this incredibly desirable field.  Back
 
Keywords:
Accelerated Analytics, Tools and Libraries, Data Center and Cloud Infrastructure, Finance, GTC Silicon Valley 2018 - ID S8843
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Eliminating Manual Data Labeling with AI-powered Data Curation (Presented by Pure Storage)
Ben Taylor (Ziff.ai), Emily Watkins (Pure Storage)
Learn from real-world case studies where large corpora of unstructured data were indexed and organized by deep-learning pipelines. Organizations are capturing and saving exponentially more unstructured data. As a tactic to organize this data, many te ...Read More
Learn from real-world case studies where large corpora of unstructured data were indexed and organized by deep-learning pipelines. Organizations are capturing and saving exponentially more unstructured data. As a tactic to organize this data, many teams turn to manual data classification, but that human-in-the-loop process can be cost prohibitive and introduce metadata inaccuracies. By applying deep learning and cluster-based labeling, we can index petabyte-scale datasets and rapidly organize unstructured data for downstream model building and analysis. This session will teach you how to quickly switch to training on all the contents of your data lake, rather than just a subset. We will use cases studies with real-world datasets to walk through best practices for a deep learning indexing pipeline.  Back
 
Keywords:
Accelerated Analytics, Data Center and Cloud Infrastructure, GTC Silicon Valley 2018 - ID S8962
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Improving the Brick and Mortar Retail Customer Experience with GPUs
Trung Tran (Clarcepto Inc)
There is a clear opportunity for retailers to generate loyalty and increase sales by focusing on the overall customer experience. We'll describe how we are developing solutions to track customer activity and build profiles based on physical store ac ...Read More
There is a clear opportunity for retailers to generate loyalty and increase sales by focusing on the overall customer experience. We'll describe how we are developing solutions to track customer activity and build profiles based on physical store activity to personalize the in-store shopping experience. We'll also describe how GPUs and deep learning are used to create these capabilities ? all while protecting personal information and privacy.  Back
 
Keywords:
Accelerated Analytics, Intelligent Video Analytics and Smart Cities, Data Center and Cloud Infrastructure, Consumer Engagement and Personalization, Computer Vision, GTC Silicon Valley 2018 - ID S8144
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Flexible and Fast Machine Learning and Deep Learning with Alluxio
Yupeng Fu (Alluxio), Michael Wendt (NVIDIA)
With the exponentially-growing deluge of data today, data lakes are pooling everywhere. So, how can you harness them for critical insights and is there an easy way to tap into the multitude of different storage systems that they''re stored in? Enter ...Read More
With the exponentially-growing deluge of data today, data lakes are pooling everywhere. So, how can you harness them for critical insights and is there an easy way to tap into the multitude of different storage systems that they''re stored in? Enter Alluxio, an agnostic and fast storage abstraction, which, when paired with deep learning and GPU-accelerated analytics yields a quick and easy way to harness the data. Join NVIDIA''s Applied Solutions Engineering (ASE) team as they walk through how to use Alluxio for fun and profit.  Back
 
Keywords:
Accelerated Analytics, GTC Silicon Valley 2018 - ID S8569
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Additive Manufacturing
Presentation
Media
Deep Learning at the Edge (Presented by HP Inc.)
Bruce Blaho (HP Inc.)
Come see how to do deep learning development on your desktop, with examples from 3D Printing and Geoscience.  In this session you will see how new powerful workstations are being used to create advanced deep learning solutions at the edge of the ...Read More
Come see how to do deep learning development on your desktop, with examples from 3D Printing and Geoscience.  In this session you will see how new powerful workstations are being used to create advanced deep learning solutions at the edge of the network - and why this is a strong complement to cloud-only based approaches.  We''ll share detailed, concrete examples from expert speakers: CGG is a geoscience company leading the use of deep learning to interpret terabyte-sized seismic data sets to find important underground features such as oil & gas deposits. HP''s Jet Fusion 3D Printers use complex thermal control systems to optimize part printing and material properties.  We will explore the deep learning based algorithms being developed for HP''s next generation of 3D printers.   Bruce Blaho - Fellow & Workstations Chief Technologist, HP Inc. Steve Dominguez - Team Lead Seismic Interpretation Software, CGG Dr. Jun Zeng - Principal Investigator, HP Labs Print & 3D, HP Inc. Dr. He Luan - Research Scientist, HP Labs Print & 3D, HP Inc.  Back
 
Keywords:
Additive Manufacturing, Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S81044
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Advanced AI Learning Techniques (incl. GANs and NTMs)
Presentation
Media
Deep Learning of Severe Weather Forecast Data
David Gagne (National Center for Atmospheric Research)
Attendees will learn how deep learning models identify severe weather hazards, how deep learning severe weather diagnosis compares with other machine learning methods, and what weather features deep learning considers most important for determining w ...Read More
Attendees will learn how deep learning models identify severe weather hazards, how deep learning severe weather diagnosis compares with other machine learning methods, and what weather features deep learning considers most important for determining whether a storm will produce severe weather or not. Severe weather hazards, such as tornadoes, hail, high winds, and flash floods, cause billions of dollars in property damage and injure or kill hundreds of people in the U.S. each year. Improved forecasts of the potential for severe weather enables decision makers to take actions to save lives and property. Machine learning and deep learning models extract spatial information from observations and numerical weather prediction model output to predict the probability of severe weather based on whether or not some form of severe weather was reported by the public. Convolutional neural networks and generative adversarial networks are compared against principal component analysis encodings to determine how much skill deep learning adds over traditional methods. The deep learning models are interrogated to identify important variables and spatial features for severe weather prediction.  Back
 
Keywords:
Advanced AI Learning Techniques (incl. GANs and NTMs), Climate, Weather, Ocean Modeling, HPC and AI, GTC Silicon Valley 2018 - ID S8455
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Disrupting Logistics and Optimization with AI
Karim Beguir (InstaDeep)
In this talk, you will get a detailed yet accessible look at how AI is disrupting logistics. Firms have for years been using classical optimization algorithms to make decisions such as how to deliver goods to multiple clients in a city, place package ...Read More
In this talk, you will get a detailed yet accessible look at how AI is disrupting logistics. Firms have for years been using classical optimization algorithms to make decisions such as how to deliver goods to multiple clients in a city, place packages in a warehouse or route orders. Such algorithms are often built on heuristics which experts have designed to get reasonable solutions quickly. Recent advances in Deep Learning and Reinforcement learning are however making it possible to build AI systems that tackle these optimization problems from scratch. Through constant learning, a modern AI system can match and even beat existing optimization algorithms, or deliver faster solutions thanks to GPU parallel processing. Companies can now leverage these advances into significant efficiency gains for their operations.  Back
 
Keywords:
Advanced AI Learning Techniques (incl. GANs and NTMs), Performance Optimization, NVIDIA Inception Program, Inventory, GTC Silicon Valley 2018 - ID S8432
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Application of Generative Deep Neural Networks for Mass Customization of Patient-Specific Products
Sergei Azernikov (Glidewell Dental), Jyh-Jing Hwang (UC Berkeley)
We''ll show how generative adversarial networks (GANs) running on GPUs are about to revolutionize mass customization of patient-specific products at Glidewell Dental. Every day, our labs produce thousands of patient-specific items, such as dental res ...Read More
We''ll show how generative adversarial networks (GANs) running on GPUs are about to revolutionize mass customization of patient-specific products at Glidewell Dental. Every day, our labs produce thousands of patient-specific items, such as dental restorations, implants, and appliances. To deliver functional and aesthetic products, high levels of precision and consistency are essential. Traditionally, dental restoration design and manufacturing process was very labor intensive and required highly skilled dental professionals. Today, with the advances in CAD/CAM, the amount of manual labor has been significantly reduced; however, there are still many aspects of the process that require human intervention due to the fact that some of these aspects are hard to formalize and therefore impossible to automate with traditional tools. The convergence of several technologies, such as deep learning, GPGPU, and cloud computing, has allowed us to effectively train generative models on historical data. These models are now capable of automatically generating high-quality patient-specific designs.  Back
 
Keywords:
Advanced AI Learning Techniques (incl. GANs and NTMs), Consumer Engagement and Personalization, GTC Silicon Valley 2018 - ID S8155
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Learning to Learn, Deep Learning for Robotics, Deep Reinforcement Learning, AI for Manufacturing and Logistics
Pieter Abbeel (UC Berkeley / OpenAI / Gradescope)
We''ll introduce the latest advances on topics such as learning-to-learn, meta-learning, deep learning for robotics, deep reinforcement learning, and AI for manufacturing and logistics. ...Read More

We''ll introduce the latest advances on topics such as learning-to-learn, meta-learning, deep learning for robotics, deep reinforcement learning, and AI for manufacturing and logistics.

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Keywords:
Advanced AI Learning Techniques (incl. GANs and NTMs), IoT, Robotics & Drones, Autonomous Machines, Robotics & Autonomous Machines, GTC Silicon Valley 2018 - ID S8118
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Algorithms and Numerical Techniques
Presentation
Media
Capture Sparsity in DL Applications
Michael Frumkin (NVIDIA)
We'll present a new technique for improving efficiency of inference and training in deep learning in the presence of sparse workloads. We'll start with a brief overview of applications of sparse linear algebra in engineering and data analysis. Then ...Read More
We'll present a new technique for improving efficiency of inference and training in deep learning in the presence of sparse workloads. We'll start with a brief overview of applications of sparse linear algebra in engineering and data analysis. Then, we'll analyze the presence of sparsity in both the training and inference phases of deep learning. To exploit this sparsity, we present our method of improving memory locality of sparse applications. We'll establish lower and upper bounds for sparse matrix operations and crossover with dense matrix operations. We'll demonstrate how to minimize memory traffic by tiling matrix operations, efficient use of L2, L1, and SMEM. We'll conclude with a performance comparison of our method with existing techniques on some real pruned weight matrices from GoogLeNet and OpenNMT's multiway translation network. This is the joint work of Michael Frumkin, Jeff Pool, and Lung Sheng Chien.  Back
 
Keywords:
Algorithms and Numerical Techniques, Performance Optimization, HPC and AI, GTC Silicon Valley 2018 - ID S8458
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World's Fastest Machine Learning With GPUs
Jonathan McKinney (H2O.ai), Rory Mitchell (H2O.ai)
Deep learning algorithms have benefited greatly from the recent performance gains of GPUs. However, it has been unclear whether GPUs can speed up machine learning algorithms such as generalized linear modeling, random forests, gradient boosting ...Read More

Deep learning algorithms have benefited greatly from the recent performance gains of GPUs. However, it has been unclear whether GPUs can speed up machine learning algorithms such as generalized linear modeling, random forests, gradient boosting machines, and clustering. H2O.ai, the leading open source AI company, is bringing the best-of-breed data science and machine learning algorithms to GPUs. We introduce H2O4GPU, a fully featured machine learning library that is optimized for GPUs with a robust python API that is a drop dead replacement for scikit-learn. We'll demonstrate benchmarks for the most common algorithms relevant to enterprise AI and showcase performance gains as compared to running on CPUs.

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Keywords:
Algorithms and Numerical Techniques, NVIDIA Inception Program, Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S8523
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CUTLASS: Software Primitives for Dense Linear Algebra at All Levels and Scales within CUDA
Andrew Kerr (NVIDIA)
Audience members will learn how to implement efficient Deep Learning computations using CUDA C++ in the context of CUTLASS. CUTLASS is an open-source collection of C++ template abstractions for implementing high-performance matrix-multiplication (GE ...Read More
Audience members will learn how to implement efficient Deep Learning computations using CUDA C++ in the context of CUTLASS. CUTLASS is an open-source collection of C++ template abstractions for implementing high-performance matrix-multiplication (GEMM) at all levels of the CUDA thread hierarchy. We will describe many of the algorithmic strategies used by cuBLAS and cuDNN, and how they can be implemented using C++ templates to cover an extensive space of problem sizes, data layouts, and data types. In particular, we will emphasize how to support alternative and mixed precision math operations such as Pascal's integer DP4A operation and Volta's TensorCores. Finally, we will illustrate how CUTLASS primitives can be combined with custom functionality to implement related algorithms such as convolution. Although this talk highlights CUTLASS, the architecture concepts and algorithm details are relevant to any CUDA programmer focused on Deep Learning.  Back
 
Keywords:
Algorithms and Numerical Techniques, Tools and Libraries, GTC Silicon Valley 2018 - ID S8854
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Datasets and Algorithms for Road Identification Via Satellite Imagery
Adam Van Etten (In-Q-Tel)
Road identification and route prediction in near real time remains a challenging problem for many geographic regions, particularly in the case of natural disasters or crisis situations. Existing methods such as manual road labeling or aggregatio ...Read More

Road identification and route prediction in near real time remains a challenging problem for many geographic regions, particularly in the case of natural disasters or crisis situations. Existing methods such as manual road labeling or aggregation of mobile GPS track data are currently insufficient in dynamic scenarios. The frequent revisits of satellite imaging constellations may accelerate efforts to rapidly update road network and optimal path prediction, provided routing information can be extracted from imaging pixels. We'll demonstrate deep learning segmentation methods for identifying road center lines and intersections from satellite imagery, and inferring networks from these road segments. We'll also explore data quality requirements by comparing open source labels with-high precision labels created as part of the SpaceNet Roads challenge.

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Keywords:
Algorithms and Numerical Techniques, HD Mapping, Federal, GTC Silicon Valley 2018 - ID S8384
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Physics-Based AI for Semiconductor Inspection Using a GPU Based Optical Neural Network (ONN)
Jing Zhang (KLA-Tencor)
We'll start with a brief background of modern semiconductor yield challenges and KLA-Tencor's solutions in the space of inspection and metrology with an emphasis on it is physics-based machine learning approaches. With the shrinking of the critical ...Read More
We'll start with a brief background of modern semiconductor yield challenges and KLA-Tencor's solutions in the space of inspection and metrology with an emphasis on it is physics-based machine learning approaches. With the shrinking of the critical dimension of integrated circuits for every generation, the inspection and metrology for semiconductor process control are facing increasingly challenges from physics limitations. As a solution, KLA-Tencor developed the physics-based AI technologies by combining traditional physical simulation with deep learning to enable a balanced solution between resolution enhancement and computational cost. We'll cover the concepts of incorporating optical physics inside a neural network implemented on GPUs, which we call an ONN (Optical Neural Network).  Back
 
Keywords:
Algorithms and Numerical Techniques, Industrial Inspection, GTC Silicon Valley 2018 - ID S8959
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Image Data Augmentation on GPU: One Method That Does It All
Tim Zaman (NVIDIA)
Data augmentation is an effective method to boost your deep-learning training performance. There are many ways of doing this augmentation, and the ways to do so are not well established, and not all deep learning frameworks support augmentation nativ ...Read More
Data augmentation is an effective method to boost your deep-learning training performance. There are many ways of doing this augmentation, and the ways to do so are not well established, and not all deep learning frameworks support augmentation natively. We present a method of doing data augmentation that is based on transformation matrices to perturb both space and color, in a way that is easy to use and understand, framework-agnostic, and fast (runs on GPU). This method works especially well for augmentations that need to be applied to both images and labels, typical in object detection and segmentation tasks. Image augmentation is a job that GPU's excel at, and it will significantly reduce the load, and need, for a fast CPU.  Back
 
Keywords:
Algorithms and Numerical Techniques, Deep Learning and AI Frameworks, Video and Image Processing, GTC Silicon Valley 2018 - ID S8380
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New Frontiers for Dense Linear Solvers: Towards Extreme Performance and Energy Efficiency
Ahmad Abdelfattah (Innovative Computing Laboratory University of Tennessee), Azzam Haidar (Innovative Computing Laboratory, University of Tennessee)
Learn how to develop fast and energy-efficient linear solvers using GPUs. Hybrid CPU-GPU techniques achieve high performance at the cost of extra power consumption. The new advancements in GPU architectures enable full GPU solutions that are high per ...Read More
Learn how to develop fast and energy-efficient linear solvers using GPUs. Hybrid CPU-GPU techniques achieve high performance at the cost of extra power consumption. The new advancements in GPU architectures enable full GPU solutions that are high performance, energy efficient, and CPU-independent. In addition, new technologies such as half precision arithmetic (FP16) help the design of new solvers that are significantly faster and even more energy efficient. While FP16 arithmetic has been a powerful tool for deep learning applications, our designs show that it is also very useful for boosting performance and energy efficiency of linear solvers. The new developments complement the hybrid algorithms in the MAGMA library, and provide users with a wide variety of designs that fit different requirements of performance, energy efficiency, and numerical accuracy.  Back
 
Keywords:
Algorithms and Numerical Techniques, Performance Optimization, GTC Silicon Valley 2018 - ID S8478
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Astronomy and Astrophysics
Presentation
Media
The SETI Institute: Using GPUs for Systems Science, Technology, and Exploration
Nathalie A. Cabrol (Seti Institute), Graham Mackintosh (NASA-STC, SETI Institute)
The SETI Institute (SI) approaches the question of the origin and nature of life in the universe. Our NASA Astrobiology Institute team develops new exploration strategies and detection methods to support the search for biosignatures on Mars and other ...Read More
The SETI Institute (SI) approaches the question of the origin and nature of life in the universe. Our NASA Astrobiology Institute team develops new exploration strategies and detection methods to support the search for biosignatures on Mars and other planets. SI is also driving a new paradigm for the exploration of biosignatures and signs of technology at all scales, using a holistic approach. This new direction requires the rapid analysis of vast amounts of data. In this presentation, we'll describe the history, successes, and challenges to current approaches, and describe SI's current and future efforts in FDL and other areas to incorporate AI and deep learning to drive this new big data paradigm for finding life in the universe.  Back
 
Keywords:
Astronomy and Astrophysics, In-Situ and Scientific Visualization, GTC Silicon Valley 2018 - ID S81023
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Autonomous Vehicles
Presentation
Media
The Road From GPU-Powered Prototypes to Production-Ready ECUs
Christoph Herzog (Elektrobit Automotive GmbH), Alexander Much (Elektrobit Automotive GmbH)
GPUs provide power-efficient hardware acceleration for graphics processing and deep learning algorithms, making them the ideal compute processors for highly automated driving functionalities. Despite the predominance of GPUs in the development of pro ...Read More
GPUs provide power-efficient hardware acceleration for graphics processing and deep learning algorithms, making them the ideal compute processors for highly automated driving functionalities. Despite the predominance of GPUs in the development of prototypes, the actual market penetration of GPUs in series-production electronic control units (ECUs) remains comparably low. In this talk we will focus on a key contributor to this problem: deficient support for integration into the design processes of the automotive supply chain and automotive software standards.  Back
 
Keywords:
Autonomous Vehicles, GPU Virtualization, GTC Silicon Valley 2018 - ID S8851
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Autoware on NVIDIA DRIVE: The Open-Source Self-Driving Platform
Shinpei Kato (Tier IV, Inc.)
We'll present a complete open-source software stack for self-driving vehicles, called Autoware, and its open integration with the NVIDIA DRIVE platform. Autoware implements working modules of localization and 3D mapping with LiDAR and GNSS, ...Read More

We'll present a complete open-source software stack for self-driving vehicles, called Autoware, and its open integration with the NVIDIA DRIVE platform. Autoware implements working modules of localization and 3D mapping with LiDAR and GNSS, object detection and traffic light recognition with deep learning, path planning with lattice and search methods, and vehicle dynamics control. Compute-intensive tasks of these modules are accelerated by using CUDA, and timing-aware tasks are protected by RTOS capabilities. We'll discuss the impact of CUDA acceleration on self-driving vehicles and its performance evaluation. Learn how Autoware enables any by-wire vehicles to become high-quality self-driving vehicles that can operate in real-world environments.

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Keywords:
Autonomous Vehicles, Autonomous Driving, GTC Silicon Valley 2018 - ID S8636
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What it Takes to Drive Autonomously on Chinese roads
Yiming Liu (Pony.ai)
Pony.ai will share the key technological milestones it has achieved in the past several months of road testing in China, including the company's soft launch of China's first-ever autonomous vehicle robotaxi service. CEO James Peng will s ...Read More

Pony.ai will share the key technological milestones it has achieved in the past several months of road testing in China, including the company's soft launch of China's first-ever autonomous vehicle robotaxi service. CEO James Peng will share the unique challenges posed by a Chinese road environment and how we leveraged deep learning and computational models to conquer those challenges. Pony.ai's mission is to build the safest and most reliable L4 autonomous driving technology. The startup was founded at the end of 2016 and is co-located in the heart of Silicon Valley and China.

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Keywords:
Autonomous Vehicles, NVIDIA Inception Program, Autonomous Driving, GTC Silicon Valley 2018 - ID S8995
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Deep Learning for Automated Systems: From the Warehouse to the Road
Melissa Smith (Clemson University)
Learn about our application of deep learning techniques for perception systems in autonomous driving, reinforcement learning for autonomous systems, label detection in warehouse inventory management, and undergraduate engagement in this research ...Read More

Learn about our application of deep learning techniques for perception systems in autonomous driving, reinforcement learning for autonomous systems, label detection in warehouse inventory management, and undergraduate engagement in this research. In collaboration with Clemson University''s International Center for Automotive Research, we''ve developed a perception module that processes camera inputs to provide environmental information for use by a planning module to actively control the autonomous vehicle. We''re extending this work to include an unsupervised planning module for navigation with reinforcement learning. We''ve also applied these techniques to automate the job of warehouse inventory management using a deep neural network running on a mobile, embedded platform to automatically detect and scan labels and report inventory, including its location in the warehouse. Finally, we''ll discuss how we involve undergraduate students in this research.

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Keywords:
Autonomous Vehicles, GTC Silicon Valley 2018 - ID S8140
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Computational Biology and Chemistry
Presentation
Media
Prediction of Heterodimeric Protein Complexes from Protein-Protein Interaction Networks Using Deep Learning
Peiying Ruan (NVIDIA)
We'll present how to apply deep learning to predict small-sized protein complexes with multiple biological information and hybrid deep learning model. We'll describe the background of the problem, what kind of biological information are u ...Read More
We'll present how to apply deep learning to predict small-sized protein complexes with multiple biological information and hybrid deep learning model. We'll describe the background of the problem, what kind of biological information are useful for accurately predicting small-sized protein complexes, how to improve the prediction accuracy by using hybrid deep learning models for different information, and compare the performance of multiple deep learning models for this problem.  Back
 
Keywords:
Computational Biology and Chemistry, Bioinformatics & Genomics, GTC Silicon Valley 2018 - ID S8333
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Deep Learning for Molecular Docking
David Koes (University of Pittsburgh)
Molecular docking is an important tool for computational drug discovery that aims to predict the binding pose of a ligand (drug) to a target protein. Identifying a correctly oriented pose requires a scoring function that has a global optimum close to ...Read More
Molecular docking is an important tool for computational drug discovery that aims to predict the binding pose of a ligand (drug) to a target protein. Identifying a correctly oriented pose requires a scoring function that has a global optimum close to the experimentally observed pose. Additionally, it should also be differentiable with respect to atomic positions so that it can be used for gradient-based pose optimization. We'll describe a differentiable grid-based convolutional neural network scoring function and explore its application in an end-to-end GPU-optimized molecular docking workflow. We'll show that convolutional neural networks trained on experimental data can successfully identify correct binding modes and meaningfully rank and score compounds. We'll also describe several visualization approaches that map the CNN score back to the atomic inputs to help guide medicinal chemistry optimization and provide insight into the functioning of the neural network. The entirety of our approach is available under an open-source license as part of our gnina package (https://github.com/gnina).  Back
 
Keywords:
Computational Biology and Chemistry, Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S8540
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Computational Physics
Presentation
Media
Breakthroughs in Astrophysics Enabled by NVIDIA GPU Technology
Brant Robertson (UC Santa Cruz)
The vast scales and complex physics of the universe pose a significant challenge for understanding how galaxies form and evolve. Theoretical astrophysicists attempt to model the physical processes that drive the formation of galaxies and other struct ...Read More
The vast scales and complex physics of the universe pose a significant challenge for understanding how galaxies form and evolve. Theoretical astrophysicists attempt to model the physical processes that drive the formation of galaxies and other structures via supercomputer simulations, but the fidelity of these simulations are limited by computational power. With the advent of supercomputers powered by NVIDIA GPUs, astrophysical simulations have taken giant strides forward in their ability to model and understand the detailed properties of galaxies. I review some of our progress enabled by NVIDIA GPUs, including large-scale GPU-powered hydrodynamical simulations and Deep Learning applied to enormous astronomical surveys of galaxies.  Back
 
Keywords:
Computational Physics, HPC and Supercomputing, GTC Silicon Valley 2018 - ID S8677
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Accelerated Deep Learning Discovery in Fusion Energy Science
William Tang (Princeton University)
Deep learning/artificial intelligence methods are increasingly being deployed to enable new avenues of big-data-driven discovery in key scientific application areas such as the quest to deliver Fusion Energy identified by the 2015 CNN "Moon ...Read More

Deep learning/artificial intelligence methods are increasingly being deployed to enable new avenues of big-data-driven discovery in key scientific application areas such as the quest to deliver Fusion Energy identified by the 2015 CNN "Moonshots for the 21st Century" series as one of 5 prominent modern grand challenges. Princeton University''s associated R&D methods have been successfully applied to accelerate progress in reliably predicting and avoiding large-scale losses (called "disruptions") of the thermonuclear plasma fuel in magnetically-confined devices the largest of which is the $25B international ITER device a burning plasma experiment under construction with the potential to exceed "breakeven" fusion power (i.e., "power out = power in") by a factor of 10 or more.

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Keywords:
Computational Physics, GTC Silicon Valley 2018 - ID S81002
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Computer Vision
Presentation
Media
SmartSense: Real-Time, Field-Deployed CV Traffic Analysis System
Justin Eichel (Miovision)
Miovision presents a video-based traffic analytics system, capable of tracking and classifying vehicles in real time throughout cities. The system leverages Jetson TX2 modules and inferencing to accurately classify vehicles at over 50 frames per ...Read More

Miovision presents a video-based traffic analytics system, capable of tracking and classifying vehicles in real time throughout cities. The system leverages Jetson TX2 modules and inferencing to accurately classify vehicles at over 50 frames per second using single-shot multibox detection and DAC, a VGG-based network. We'll cover many of the issues our teams went through to design and implement the system, including data collection, annotation, training, incorporating continuous training, and deep learning iteration. We'll also illustrate how the measured traffic trends were used to reduce congestion and evaluate the health of traffic corridors.

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Keywords:
Computer Vision, Intelligent Video Analytics and Smart Cities, Autonomous Machines, GTC Silicon Valley 2018 - ID S8383
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Using Multimodal Learning for TV Show Summarization
Yonghua Lin (IBM Research China), Qing Wang (IBM Research China)
We'll explore new techniques for TV show summarization using multimodal deep learning for saliency detection and fusion. For TV show summarization, the goal is to compact visual summary with informativeness and enjoyability to attract audience. In o ...Read More
We'll explore new techniques for TV show summarization using multimodal deep learning for saliency detection and fusion. For TV show summarization, the goal is to compact visual summary with informativeness and enjoyability to attract audience. In our work, we propose a multimodal summarization platform to integrate the multimodal saliences learned from video, audio, and text. Our work focuses on three aspects: 1) the saliency extraction for video, audio, and text using deep learning networks; 2) fusion framework design for multimodal information integration; 3) developing tools to speed up video processing. Using AI Vision, which is a public cloud-based AI service, we summarize a TV show with 11 hours duration in one minute.  Back
 
Keywords:
Computer Vision, Intelligent Video Analytics and Smart Cities, Video and Image Processing, GTC Silicon Valley 2018 - ID S8221
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Advancing Representation Learning for Language and Vision
Hideki Nakayama (The University of Tokyo)
As an NVAIL partner, Machine Perception Group at Tokyo university is focusing on various research areas of AI, particularly on natural language processing, computer vision, and their cross-disciplinary domain. Since deep learning has revolutionalized ...Read More
As an NVAIL partner, Machine Perception Group at Tokyo university is focusing on various research areas of AI, particularly on natural language processing, computer vision, and their cross-disciplinary domain. Since deep learning has revolutionalized all these fields, one of the core issues has been how to effectively extract powerful semantic representations from low-level inputs in an end-to-end manner. Indeed, remarkable progress has been made on this point in recent years, enabling many spectacular cross-modal applications. In this talk, we will introduce several research projects in our group related to representation learning for language and vision, and discuss future direction.  Back
 
Keywords:
Computer Vision, Speech and Language Processing, GTC Silicon Valley 2018 - ID S8683
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Accelerated Functional Mapping of World with NVIDIA GPUs and Deep Learning
Christopher Layton (Oak Ridge National Laboratory), Dalton Lunga (Oak Ridge National Laboratory), H. Lexie Yang (Oak Ridge National Laboratory)
The functional mapping of man-made facilities from high-resolution remote sensing images provides timely high-fidelity land-use information and population distribution estimates, which facilitates federal, non-governmental agency and industrial expan ...Read More
The functional mapping of man-made facilities from high-resolution remote sensing images provides timely high-fidelity land-use information and population distribution estimates, which facilitates federal, non-governmental agency and industrial expansion efficiency. We'll share our journey to deliver functional maps of the world that include building extraction, human settlement maps, mobile home parks, and facility mapping using a variety of remote sensing imagery. Our research addresses three frontier challenges; 1) distinct characteristics of remote sensing data for deep learning (including the model distribution shifts encountered with remote sensing images), multisensor sources, and data multi modalities; 2) training very large deep-learning models using multi-GPU and multi-node HPC platforms; 3) large-scale inference using ORNL's Titan and Summit with NVIDIA TensorRT. We'll also talk about developing workflows to minimize I/O inefficiency, doing parallel gradient-descent learning, and managing remote sensing data in HPC environment.  Back
 
Keywords:
Computer Vision, GIS, HPC and AI, GTC Silicon Valley 2018 - ID S8420
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Cyber Security
Presentation
Media
Analyzing Sequences of Time Series Security Data with Recurrent Residual Networks
Ivko Cvejic (US Bank), Leon DeFrance (US Bank)
Analyzing time series data from security controls for signs of malicious activity is a common challenge in financial networks. We show how one tool, a recurrent residual deep learning (DL) model, can be used to rapidly analyze variable-length time se ...Read More
Analyzing time series data from security controls for signs of malicious activity is a common challenge in financial networks. We show how one tool, a recurrent residual deep learning (DL) model, can be used to rapidly analyze variable-length time series data to achieve meaningful analysis. Recurrent networks have long been a popular choice in DL for analyzing data with multiple time-steps where the meaning of data at one point in time is dependent upon data at other time-steps. For example, natural language processing solutions frequently utilize recurrent DL models to achieve state-of-the-art results in classification tasks. However, recurrent models are often plagued by issues concerning training difficulty as a function of the model depth. These issues are often exacerbated by the desire to create very deep models for particularly difficult tasks. Utilizing the ResNet concept developed by Microsoft research applied to a recurrent model, we show how models analyzing large sequences can achieve state-of-the-art results with fewer parameters and faster training times.  Back
 
Keywords:
Cyber Security, Finance, GTC Silicon Valley 2018 - ID S8656
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Data Center and Cloud Infrastructure
Presentation
Media
Impact of Storage System Performance on TensorFlow Data Ingestion
Mark Whitney (Rescale)
As multi-GPU deep learning performance improves, the performance of the storage system hosting a dataset becomes critical in keeping these GPUs fully utilized. We survey the different methods for providing training data to a TensorFlow application on ...Read More
As multi-GPU deep learning performance improves, the performance of the storage system hosting a dataset becomes critical in keeping these GPUs fully utilized. We survey the different methods for providing training data to a TensorFlow application on a GPU, and benchmark data throughput for a variety of popular neural network architectures. We look at performance and potential bottlenecks for local storage technologies (SCSI SSD and NVMe), high performance network-attached file systems, TensorFlow native connectors (HDFS and S3), and FUSE-connected object storage.  Back
 
Keywords:
Data Center and Cloud Infrastructure, NVIDIA Inception Program, HPC and AI, GTC Silicon Valley 2018 - ID S8544
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High-Performance Input Pipelines for Scalable Deep Learning
Brian Gold (Pure Storage)
Learn how to keep your GPUs fed with data as you train the next-generation of deep learning architectures. As GPU technology continues to advance, the demand for faster data continues to grow. In deep learning, input pipelines are responsible for a c ...Read More
Learn how to keep your GPUs fed with data as you train the next-generation of deep learning architectures. As GPU technology continues to advance, the demand for faster data continues to grow. In deep learning, input pipelines are responsible for a complex chain of actions that ultimately feed data into GPU memory: defining how files are read from storage, deserializing them into data structures, pre-processing on a CPU, and copying to the GPU. These pipelines bring together complex hardware systems--including cluster networks, peripheral interconnects, modern CPUs, and storage devices--along with sophisticated software systems to drive the data movement and transformation. In this talk, we present a new benchmark suite for evaluating and tuning input pipelines. We will examine results with TensorFlow's DataSets API on a DGX-1 with V100 and provide guidance on key tuning parameters and diagnostic techniques for improving performance.  Back
 
Keywords:
Data Center and Cloud Infrastructure, Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S8948
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Pooling and Orchestrating NVIDIA Jetson for AI and Deep Learning on the Edge
Sumit Puri (Liqid Inc.)
Attendees will learn how NVIDIA''s Jetson TX-series processors can be scaled out to create an adaptive HPC and Supercomputing platform for bespoke deployments and edge computing environments. Advancements in composable infrastructure technology now m ...Read More
Attendees will learn how NVIDIA''s Jetson TX-series processors can be scaled out to create an adaptive HPC and Supercomputing platform for bespoke deployments and edge computing environments. Advancements in composable infrastructure technology now make it possible to pool and orchestrate Jetson processors for deployments with specialized parallel computing requirements. Use cases include Jetson deployments in non-embedded environments for edge computing where traditional HPC architectures are not hospitable. Clusters of NVIDIA Jetson TX- devices can be deployed in edge compute environments connected to arrays of sensors for neural net training, pattern recognition, and deep learning. Applications for autonomous transportation can also benefit from clustering massive numbers of Jetson TX- devices to simulate fleets of vehicles to train machine learning algorithms in parallel. Jetson use cases can be expanded well beyond embedded applications when deployed with PCIe-based fabric composable infrastructure technology, permitting 16x networking performance improvement over the embedded 1Gb Ethernet interface.  Back
 
Keywords:
Data Center and Cloud Infrastructure, Graphics and AI, GTC Silicon Valley 2018 - ID S8539
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Advantages of a Bare-Metal Cloud for CUDA Workloads (Presented by Oracle)
Karan Batta (Oracle)
With traditional performance intensive workloads transitioning to the cloud and new workloads such as deep learning relying on cloud resources, it''s imperative to optimize the environment to squeeze every ounce of performance from the NVIDIA GPUs as ...Read More
With traditional performance intensive workloads transitioning to the cloud and new workloads such as deep learning relying on cloud resources, it''s imperative to optimize the environment to squeeze every ounce of performance from the NVIDIA GPUs as possible. Learn how levers like bare-metal servers, a true flat network and high-performance storage can really accelerate workloads utilizing NVIDIA GPUs in the cloud. See live demos and walkthrough of how easy it is to launch your very own GPU cluster in Oracle Cloud Infrastructure. Additionally learn about new announcements on Oracle Cloud Infrastructure in partnership with NVIDIA. This is a session not to be missed!  Back
 
Keywords:
Data Center and Cloud Infrastructure, GTC Silicon Valley 2018 - ID S8978
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The Path to GPU as a Service in Kubernetes
Viraj Chavan (NVIDIA), Renaud Gaubert (NVIDIA)
Kubernetes modern production patterns for Deep Learning applications and a deep dive into the Kubernetes GPU subsystem and its challenges (performance, scheduling, monitoring). Autonomous vehicles, face recognition, High Performance Computing, Virtua ...Read More
Kubernetes modern production patterns for Deep Learning applications and a deep dive into the Kubernetes GPU subsystem and its challenges (performance, scheduling, monitoring). Autonomous vehicles, face recognition, High Performance Computing, Virtual Reality, NVIDIA GPUs are enabling a new computer era with cloud computing at its center. With kubernetes being the next iteration in cloud technologies, the NVIDIA container team with the kubernetes community is driving the advances in GPU integration. During this talk we will review how to deploy a GPU enabled Kubernetes and the modern production patterns for deploying GPU enabled services and applications. We will also dive into the details of the Kubernetes device plugin (its GPU subsystem), the NVIDIA container stack and the limitations provided by the kubernetes infrastructure. We will finally be discussing the latest improvements in the device plugin subsystem of Kubernetes, and the challenges ahead of it such as NUMA, sharing and scheduling.  Back
 
Keywords:
Data Center and Cloud Infrastructure, GTC Silicon Valley 2018 - ID S8893
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Architecting a Complete Data Infrastructure for AI and Deep Learning (Presented by NetApp)
Kesari Mishra (NetApp), Santosh Rao (NetApp)
Enterprises are eager to take advantage of artificial intelligence technologies such as deep learning to introduce new services and enhance insights from company data. As data science teams move past proof of concept and begin to operationalize deep ...Read More
Enterprises are eager to take advantage of artificial intelligence technologies such as deep learning to introduce new services and enhance insights from company data. As data science teams move past proof of concept and begin to operationalize deep learning, it becomes necessary to focus on the creation of a complete data architecture that eliminates bottlenecks to facilitate faster model iteration. Designing a data architecture involves thinking holistically about the deep learning pipeline, from data ingest and edge analytics, to data prep and training in the core data center, to archiving in the cloud. It is necessary to understand performance requirements and data services needed, but one should also consider future extensibility and supportability as deep learning hardware and cloud approaches evolve over time. This session will examine all the factors involved in the architecture of a deep learning pipeline, focusing in on data management and the hybrid cloud. Careful infrastructure planning can smooth the flow of data through your deep learning pipeline, lead to faster time to deployment, and thus maximum competitive differentiation.  Back
 
Keywords:
Data Center and Cloud Infrastructure, Performance Optimization, GTC Silicon Valley 2018 - ID S8974
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Microsoft AI and Research - Infrastructure Overview for Deep Learning and Other Research
Jim Jernigan (Microsoft Research)
Microsoft Research leverages a wide variety of open-source, free and custom tools to manage a complex infrastructure for doing research. We are in a unique position at Microsoft and in the industry, where we serve academic experts who expect access t ...Read More
Microsoft Research leverages a wide variety of open-source, free and custom tools to manage a complex infrastructure for doing research. We are in a unique position at Microsoft and in the industry, where we serve academic experts who expect access to the latest open source tools, in an environment where Microsoft solutions should also be considered. See examples of how we manage popular/constrained assets and enforce fairness across many systems. Linux/Docker, Windows, On-site, Azure, or a hybrid of all-of-the above we see it all. In this session, you will learn what tools can be easily leveraged to manage your own onsite and cloud GPU infrastructure. We touch on Cluster management fabrics, scheduling, authentication, hot storage, configuration management, software portability/container management and high-performance hardware selection.  Back
 
Keywords:
Data Center and Cloud Infrastructure, HPC and AI, GTC Silicon Valley 2018 - ID S8663
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Bare-Metal Abstractions for Modern Hardware
Cyprien Noel (UC Berkeley)
Hardware is getting smarter every day. GPUs, hardware accelerated networks, and non-volatile memories are increasingly replacing capabilities offered today by operating systems and software libraries. They are becoming available on-premise and in clo ...Read More
Hardware is getting smarter every day. GPUs, hardware accelerated networks, and non-volatile memories are increasingly replacing capabilities offered today by operating systems and software libraries. They are becoming available on-premise and in clouds. Leveraging them in your application can yield orders of magnitude improvements in latency and throughput, and much smaller code bases. We present simple abstractions exposing hardware capabilities, and work-in-progress demos: data storage using hardware erasure codes present in recent network adapters, streaming data from storage to GPUs using RDMA, and executing a deep learning distributed compute graph entirely in hardware using GPUDirect Async. Our demos are attempts to replace large code bases with few lines of Python, using interchangeable and unified hardware abstractions, so data and control events can flow directly device-to-device.  Back
 
Keywords:
Data Center and Cloud Infrastructure, Deep Learning and AI Frameworks, HPC and AI, GTC Silicon Valley 2018 - ID S8154
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Maximizing The Power of GPU For Diverse Workloads of Enterprise Digital Workspaces On VMware vSphere
Uday Kurkure (VMware), Hari Sivaraman (VMware)
Enterprise Digital Workspaces support diverse workloads including virtual desktops, deep learning, big data. Nvidia GPUs bring high performance computing (HPC) for graphics, GPGPU, especially machine learning workloads. They also provide HW encode an ...Read More
Enterprise Digital Workspaces support diverse workloads including virtual desktops, deep learning, big data. Nvidia GPUs bring high performance computing (HPC) for graphics, GPGPU, especially machine learning workloads. They also provide HW encode and decode to accelerate the processing of video contents. In this session, we will explore performance and resource utilization of various workloads that leverage different capabilities of GPU like graphics, compute and H.264 HW encode / decode. Nvidia virtualized GPUs and VMware vSphere brings in tremendous combined benefits for both GPU-based workloads and data center management via virtualization. We will present results of our research on running diverse workloads on vSphere platform using Nvidia GRID GPUs. We explore vSphere features of Suspend/Resume and vMotioning of vGPU based virtual machines. We will quantify benefits of vGPU for data center management using VMware vSphere and describe techniques for efficient management of workloads and datacenter resources.  Back
 
Keywords:
Data Center and Cloud Infrastructure, GPU Virtualization, HPC and AI, GTC Silicon Valley 2018 - ID S8250
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How to Use NGC Containers on AWS
Scott Ellis (NVIDIA), Jeffrey Weiss (NVIDIA)
We'll discuss how to use the NVIDIA GPU Cloud to easily run containerized Deep Learning applications. Come in only knowing the name NVIDIA GPU Cloud (NGC), and leave having successfully kicked off multiple Deep Learning containers. In this sess ...Read More
We'll discuss how to use the NVIDIA GPU Cloud to easily run containerized Deep Learning applications. Come in only knowing the name NVIDIA GPU Cloud (NGC), and leave having successfully kicked off multiple Deep Learning containers. In this session we'll use the WebUI to log into NGC, run jobs based on those NVIDIA containers using Volta-powered AWS instances, and explore how to customize and integrate NGC containers into a Deep Learning workflow.  Back
 
Keywords:
Data Center and Cloud Infrastructure, Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S8276
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Deep Learning and AI
Presentation
Media
Healthcare AI Startup Pitches
Jensen Huang (NVIDIA)
Watch leading Healthcare AI Startups compete for cash prizes at the NVIDIA GTC 2018 Inception Awards Finale. ...Read More

Watch leading Healthcare AI Startups compete for cash prizes at the NVIDIA GTC 2018 Inception Awards Finale.

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Keywords:
Deep Learning and AI, GPU Virtualization, GTC Silicon Valley 2018 - ID SE0008A
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Enterprise AI Startup Pitches
Jensen Huang (NVIDIA)
Watch leading Enterprise AI Startups compete for cash prizes at the NVIDIA GTC 2018 Inception Awards Finale ...Read More

Watch leading Enterprise AI Startups compete for cash prizes at the NVIDIA GTC 2018 Inception Awards Finale

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Keywords:
Deep Learning and AI, GPU Virtualization, GTC Silicon Valley 2018 - ID SE0008B
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Autonomous Systems AI Startup Pitches
Jensen Huang (NVIDIA)
Watch leading Autonomous Systems AI Startups compete for cash prizes at the NVIDIA GTC 2018 Inception Awards Finale ...Read More

Watch leading Autonomous Systems AI Startups compete for cash prizes at the NVIDIA GTC 2018 Inception Awards Finale

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Keywords:
Deep Learning and AI, GPU Virtualization, GTC Silicon Valley 2018 - ID SE0008C
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Deep Learning and AI Frameworks
Presentation
Media
Multi-GPU Accelerated Methods in Deep Reinforcement Learning
Adam Stooke (UC Berkeley)
Deep reinforcement learning (RL) has achieved many recent successes, yet experiment turn-around time remains a key bottleneck in research and in practice. We investigate how to optimize existing deep RL algorithms for modern computers, specifically f ...Read More
Deep reinforcement learning (RL) has achieved many recent successes, yet experiment turn-around time remains a key bottleneck in research and in practice. We investigate how to optimize existing deep RL algorithms for modern computers, specifically for a combination of CPUs and GPUs. We confirm that both policy gradient and Q-value learning algorithms can be adapted to learn using many parallel simulator instances. We further find it possible to train using batch sizes considerably larger than are standard, without negatively affecting sample complexity or final performance. We leverage these facts to build a unified framework for parallelization that dramatically hastens experiments in both classes of algorithm. All neural network computations use GPUs, accelerating both data collection and training. Our results include using an entire NVIDIA DGX-1 to learn successful strategies in Atari games in single-digit minutes, using both synchronous and asynchronous algorithms.  Back
 
Keywords:
Deep Learning and AI Frameworks, Tools and Libraries, Performance Optimization, GTC Silicon Valley 2018 - ID S8272
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AI and Deep Learning in R
Jared Lander (Lander Analytics)
We'll discuss use cases for machine learning on GPUs and how to implement them easily in the R programming language by walking through the ideas behind several modern techniques, including penalized regression, boosted trees, and deep nets. Along wi ...Read More
We'll discuss use cases for machine learning on GPUs and how to implement them easily in the R programming language by walking through the ideas behind several modern techniques, including penalized regression, boosted trees, and deep nets. Along with introducing the concepts briefly cover, we'll discuss some of the math behind the models and look at code examples to run the models on GPUs in R.  Back
 
Keywords:
Deep Learning and AI Frameworks, Programming Languages, GTC Silicon Valley 2018 - ID S8138
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Deep Learning for Surface Reconstruction
Shafaatunnur Hasan (UTM Big Data Centre, Universiti Teknologi Malaysia), Siti Mariyam Shamsuddin (UTM Big Data Centre, Universiti Teknologi Malaysia)
We'll present deep learning algorithm in reconstructing the surfaces from massive data points. The deep learning consists of multiple layers in organizing the neurons for optimal neighborhood representations. The implementations are done by sli ...Read More
We'll present deep learning algorithm in reconstructing the surfaces from massive data points. The deep learning consists of multiple layers in organizing the neurons for optimal neighborhood representations. The implementations are done by slicing into half the standard self organizing map (SOM) network to form multiple layers. The Z-axis distance is omitted in the computation of neighborhood distance when updating the weighted neurons to avoid surface points discontinuity due the layers depth. In this scenario, the distance determining the winning node is computed using 2D calculation from four directions. As the layers increase, the complexity computations arise, and the processing power should increase as well. Thus, we implement CUDA programming to update the weights and distance of the winning node. Reduction techniques are implemented to obtain the smallest distance for the winning node. For weight updating process, each thread is given several nodes to calculate the distance between the winning node and the current node. Two parts are involved in designing and developing the algorithms: point reduction and point optimization for surface reconstruction.  Back
 
Keywords:
Deep Learning and AI Frameworks, Graphics and AI, GIS, GTC Silicon Valley 2018 - ID S8425
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Node-Level Deep Learning Input Pipeline Optimization on GPGPU-Accelerated HPC Systems
Justin Fletcher (Maui High Performance Computing Center)
Learn how to implement and analyze a simple deep learning input pipeline pattern that prevents slowdowns from input queue exhaustion on accelerated HPC systems with limited impact to model performance. Queue exhaustion occurs because the throughput-d ...Read More
Learn how to implement and analyze a simple deep learning input pipeline pattern that prevents slowdowns from input queue exhaustion on accelerated HPC systems with limited impact to model performance. Queue exhaustion occurs because the throughput-driven dequeue rate is greater than the enqueue rate, which is bound by storage access bandwidth. In this session we will describe a technique that prevents queue exhaustion by artificially slowing the effective dequeue rate, without sacrificing substantial validation set performance. An example using TensorFlow is presented, and the resultant optimization step speedup and model performance are analyzed across several HPC resource configurations.  Back
 
Keywords:
Deep Learning and AI Frameworks, HPC and Supercomputing, GTC Silicon Valley 2018 - ID S8674
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Deep Learning Hyperparameter Optimization with Competing Objectives via Bayesian Optimization
Scott Clark (SigOpt)
Bayesian Optimization is an efficient way to optimize machine learning model parameters, especially when evaluating different parameters is time-consuming or expensive. Deep learning pipelines like MXnet are notoriously expensive to train, even on GP ...Read More
Bayesian Optimization is an efficient way to optimize machine learning model parameters, especially when evaluating different parameters is time-consuming or expensive. Deep learning pipelines like MXnet are notoriously expensive to train, even on GPUs, and often have many tunable parameters including hyperparameters, the architecture, and feature transformations that can have a large impact on the efficacy of the model. In traditional optimization, a single metric like accuracy is optimized over a potentially large set of configurations with the goal of producing a single, best configuration. We'll explore real world extensions where multiple competing objectives need to be optimized, a portfolio of multiple solutions may be required, constraints on the underlying system make certain configurations not viable, and more. We'll present work from recent ICML and NIPS workshop papers and detailed examples, with code, for each extension.  Back
 
Keywords:
Deep Learning and AI Frameworks, NVIDIA Inception Program, GTC Silicon Valley 2018 - ID S8136
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Doing Bayesian Deep Learning with ZhuSuan
Jiaxin Shi (Tsinghua University)
We discuss the basic concepts of Bayesian deep learning will be introduced with a hands-on tutorial that walks through several example applications using ZhuSuan (https://github.com/thu-ml/zhusuan). We'll start with simpler models like Bayesian logi ...Read More
We discuss the basic concepts of Bayesian deep learning will be introduced with a hands-on tutorial that walks through several example applications using ZhuSuan (https://github.com/thu-ml/zhusuan). We'll start with simpler models like Bayesian logistic regression, and then proceed to deeper ones like Bayesian neural networks (BNN) and variational autoencoders (VAE). Learn how to use Bayesian methods to capture uncertainty of deep learning, including modeling the data distribution, calibrating the confidence of outputs, and smoothing predictions to prevent overfitting. Real problems (e.g. regression, image generation, semi-supervised classification) will be used to illustrate the models.  Back
 
Keywords:
Deep Learning and AI Frameworks, Tools and Libraries, GTC Silicon Valley 2018 - ID S8593
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State-of-the-Art Large Scale Language Modeling in 12 Hours With a Single GPU
Nitish Shirish Keskar (Salesforce Research), Stephen Merity (Salesforce Research)
For sequence learning tasks that utilize recurrent neural networks, scale is both the key to accuracy and the bane of speed. We'll take existing state-of-the-art language modeling techniques and speed them up by orders of magnitude without losing ac ...Read More
For sequence learning tasks that utilize recurrent neural networks, scale is both the key to accuracy and the bane of speed. We'll take existing state-of-the-art language modeling techniques and speed them up by orders of magnitude without losing accuracy. The tactics include injecting flexibility into NVIDIA's black box cuDNN LSTM; replacing the LSTM with the more parallelized and customizable Quasi-Recurrent Neural Network; reducing the softmax bottleneck using the adaptive softmax; and investigating individual function efficiency on the GPU using the NVIDIA Visual Profiler. The end result is a general and scalable language model framework that can achieve state-of-the-art quality on the WikiText-103 dataset (103 million words) in under 12 hours using a single NVIDIA Volta V100. The resulting PyTorch codebase is open source for experimentation and extension.  Back
 
Keywords:
Deep Learning and AI Frameworks, Performance Optimization, GTC Silicon Valley 2018 - ID S8654
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A Low-Latency Inference System for Recurrent Neural Networks
Jinyang Li (New York University)
We'll present cellular batching, which is a new way of performing batching on GPUs to accelerate model inference for recurrent neural networks (RNNs). Existing deep learning systems perform batching by collecting a fixed set of input samples and fus ...Read More
We'll present cellular batching, which is a new way of performing batching on GPUs to accelerate model inference for recurrent neural networks (RNNs). Existing deep learning systems perform batching by collecting a fixed set of input samples and fusing their underlying dataflow graphs together for execution. This approach does not perform well for RNNs with input-dependent dataflow graphs. We propose cellular batching, which can significantly improve both the latency and throughput of RNN inference. Cellular batching performs batching at the granularity of an RNN "cell'' -- a subgraph with shared weights -- and dynamically assembles a batched block for execution as requests join and leave the system. We show that this new way of batching can reduce the inference latency by 50 to 90 percent, while also increasing the throughput by 10 to 200 percent.  Back
 
Keywords:
Deep Learning and AI Frameworks, Tools and Libraries, Performance Optimization, GTC Silicon Valley 2018 - ID S8608
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ONNX: Interoperable Deep learning (Presented by Facebook)
Dmytro Dzhulgakov (Facebook)
We'll discuss how to transfer models seamlessly from one framework to another using open neural network exchange (ONNX) from the project's lead developer. ONNX is an open specification to provide a common intermediate representation ...Read More
We'll discuss how to transfer models seamlessly from one framework to another using open neural network exchange (ONNX) from the project's lead developer. ONNX is an open specification to provide a common intermediate representation for deep learning models. This specification and set of tools, launched by Facebook, Microsoft, and Amazon, is now supported by a community of partners that includes hardware vendors, startups, and a growing number of deep learning frameworks. The ONNX ecosystem also includes support by hardware-optimized libraries such as NVIDIA's TensorRT. ONNX is the crucial first step toward an open ecosystem that empowers AI developers to choose the most effective tools for each project and accelerate AI research to production scale.   Back
 
Keywords:
Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S8818
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Research To Production: How Facebook does AI at Scale (Presented by Facebook)
Sarah Bird (Facebook), Howard Mansell (Facebook AI Research)
Facebook's strength in AI innovation comes from the ability to quickly bring cutting-edge research into large scale production using a multi-faceted toolset. We'll discuss how Facebook leverages open source software to perform truly iter ...Read More

Facebook's strength in AI innovation comes from the ability to quickly bring cutting-edge research into large scale production using a multi-faceted toolset. We'll discuss how Facebook leverages open source software to perform truly iterative AI research, scale it seamlessly for inference, and deploy it across the data center and mobile environments with ONNX. 

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Keywords:
Deep Learning and AI Frameworks, Deep Learning and AI, GTC Silicon Valley 2018 - ID S8795
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Multi-GPU Training with NCCL
Sylvain Jeaugey (NVIDIA)
We'll cover recent features and performance improvement in the NVIDIA collective communication library (NCCL). NCCL is designed to make computing on multiple GPUs easy and is integrated in most deep learning frameworks to accelerate training ...Read More

We'll cover recent features and performance improvement in the NVIDIA collective communication library (NCCL). NCCL is designed to make computing on multiple GPUs easy and is integrated in most deep learning frameworks to accelerate training times. NCCL supports communication over Shared memory, PCI, NVLink, Sockets, and InfiniBand Verbs, to support both multi-GPU machines and multi-node clusters. 

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Keywords:
Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S8462
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Extracting Data from Tables and Charts in Natural Document Formats
Philipp Meerkamp (Bloomberg), David Rosenberg (Bloomberg)
Financial analysis depends on accurate financial data, and these data are often distributed via PDF and other "natural document" formats. While these formats are optimized for easy human comprehension, automatically extracting the data can ...Read More
Financial analysis depends on accurate financial data, and these data are often distributed via PDF and other "natural document" formats. While these formats are optimized for easy human comprehension, automatically extracting the data can be quite challenging. We'll describe our work using a deep learning pipeline to extract data from tables and charts in PDF documents. We'll also show some of our latest research, inspired by image captioning models, for directly going from images of tables to a markup language (LaTeX) representation.  Back
 
Keywords:
Deep Learning and AI Frameworks, Finance, GTC Silicon Valley 2018 - ID S8651
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The Journey from a Small Development Lab Environment to a Production Datacenter for Deep Learning Applications
Ryan Olson (NVIDIA), Markus Weber (NVIDIA)
We'll do a dive deep into best practices and real world examples of leveraging the power and flexibility of local GPU workstations, such has the DGX Station, to rapidly develop and prototype deep learning applications. We'll demonstrate the setup o ...Read More
We'll do a dive deep into best practices and real world examples of leveraging the power and flexibility of local GPU workstations, such has the DGX Station, to rapidly develop and prototype deep learning applications. We'll demonstrate the setup of our small lab, which is capable of supporting a team of several developers/researchers, and our journey as we moved from lab to data center. Specifically, we'll walk through our experience of building the TensorRT Inference Demo, aka Flowers, used by Jensen to demonstrate the value of GPU computing throughout the world-wide GTCs. As an added bonus, get first-hand insights into the latest advancements coming to AI workstations this year. The flexibility for fast prototyping provided by our lab was an invaluable asset as we toyed with different software and hardware components. As the models and applications stabilized and we moved from lab to data center, we were able to run fully load-balanced over 64 V100s serving video inference demonstrating Software-in-the-Loop's (SIL) ReSim capabilities for Autonomous Vehicles at GTC EU. Real live examples will be given.  Back
 
Keywords:
Deep Learning and AI Frameworks, HPC and AI, GTC Silicon Valley 2018 - ID S8263
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Experiences of End2end Deep Learning Optimization on Alibaba PAI Deep Learning Platform
Minmin Sun (Alibaba), Jun Yang (Alibaba)
We'll share experiences of end-to-end deep learning optimization on Alibaba's platform of artificial intelligence (PAI), including both offline training and online inference. For offline training, dedicated optimization is made for local and distri ...Read More
We'll share experiences of end-to-end deep learning optimization on Alibaba's platform of artificial intelligence (PAI), including both offline training and online inference. For offline training, dedicated optimization is made for local and distributed environment. For online inference, the optimization is done through both algorithm and system perspectives. Both the methodology and benchmark number are shared during this session. We'll share several business applications driven by these optimizations to ensure learning to bridge the gap between low-level optimization and real business scenarios.  Back
 
Keywords:
Deep Learning and AI Frameworks, Performance Optimization, GTC Silicon Valley 2018 - ID S8113
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How To Train and Execute a Deep Learning Model Able to Re-identify and Extract Attributes from Humans
Matthieu Ospici (Atos)
We'll present a deep learning system able to decide if two people are similar or not. This system use the global appearance of a person, not just the face, to perform the re-identification. Our system also provides attributes (top color, bot ...Read More

We'll present a deep learning system able to decide if two people are similar or not. This system use the global appearance of a person, not just the face, to perform the re-identification. Our system also provides attributes (top color, bottom color, genre, length of the clothes, and the hair). We'll describe how to train a system with tensorflow on a GPU cluster and how to use it on a global video analysis system running on GPU devices.

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Keywords:
Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S8355
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Performance Optimization for Deep-Learning on the latest OpenPOWER systems
Khoa Huynh (IBM), Jonathan Samn (IBM), Brian Wan (IBM)
We''ll discuss how cognitive workloads could leverage the latest OpenPOWER systems with NVIDIA Volta V100 GPUs and fast NVLink 2.0 CPU-GPU interconnects. IBM has formed a close partnership with NVIDIA to offer GPU-enabled OpenPOWER systems and P ...Read More
We''ll discuss how cognitive workloads could leverage the latest OpenPOWER systems with NVIDIA Volta V100 GPUs and fast NVLink 2.0 CPU-GPU interconnects. IBM has formed a close partnership with NVIDIA to offer GPU-enabled OpenPOWER systems and PowerAI software to our customers and developers. We''ll focus on the latest OpenPOWER systems and how large-scale deep-learning neural network training could leverage the unique capabilities of these systems with PowerAI Release 4. Also discussed is the new IBM distributed deep learning (DDL) technology that allows neural network model training to scale almost linearly across hundreds of NVIDIA GPUs.  Back
 
Keywords:
Deep Learning and AI Frameworks, Performance Optimization, GTC Silicon Valley 2018 - ID S8765
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Flavors: Library of AI Powered Trie Structures for Fast Parallel Lookup
Krzysztof Kaczmarski (Warsaw University of Technology), Albert Wolant (Warsaw University of Technology)
Learn how to use deep learning to build highly optimized data structures matching your needs exactly. During this session, you will find out what can be accomplished, if you combine massively parallel data structures and modern AI techniques to achie ...Read More
Learn how to use deep learning to build highly optimized data structures matching your needs exactly. During this session, you will find out what can be accomplished, if you combine massively parallel data structures and modern AI techniques to achieve best performance for data lookup. We will present results on real life data, both from academia and industry, that will show just how flexible presented method is. We will also share insights on optimization process gained during the project.  Back
 
Keywords:
Deep Learning and AI Frameworks, Tools and Libraries, Performance Optimization, GTC Silicon Valley 2018 - ID S8401
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Beyond What is Being Said: Toward Understanding Human-to-Human Conversations
Samuel Kim (Gridspace)
Gridspace is researching human conversations using various GPU-accelerated deep learning algorithms. Our GPU-based software stack provides a novel way to process large-scale speech data; it is capable of understanding what is being said and how it is ...Read More
Gridspace is researching human conversations using various GPU-accelerated deep learning algorithms. Our GPU-based software stack provides a novel way to process large-scale speech data; it is capable of understanding what is being said and how it is being said. We''ll introduce several commercial features including speech recognition, emotion recognition, sentiment analysis, and call grading. Compared to conventional ways of dealing with large-scale speech data, the proposed method provides the platform that analyze a whole data rather than fraction of sampled data.  Back
 
Keywords:
Deep Learning and AI Frameworks, Speech and Language Processing, NVIDIA Inception Program, GTC Silicon Valley 2018 - ID S8615
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GPU Coder: Automatic CUDA and TensorRT Code Generation from MATLAB
Jaya Shankar (MathWorks), Girish Venkataramani (MathWorks)
Learn how GPU Coder produces high-performance CUDA code automatically from a high-level algorithm description in MATLAB. Write your deep learning application with the expressive power of MATLAB, which allows you to describe not just the use of your t ...Read More
Learn how GPU Coder produces high-performance CUDA code automatically from a high-level algorithm description in MATLAB. Write your deep learning application with the expressive power of MATLAB, which allows you to describe not just the use of your trained deep learning model in inference mode but also perform data-augmentation and post-processing of the results to create a complete deployment-ready application. GPU Coder can then generate optimized inference code for the whole application. The deep learning inference model is compiled down to TensorRT, while the rest of the application logic is parallelized through creation of CUDA kernels and integration with other CUDA optimized libraries like cuBLAS, cuFFT, etc. The generated code can be cross-compiled to any NVIDIA GPU device that supports TensorRT. This allows engineers and scientists to unlock the expressive ease-of-use of the MATLAB programming language while unleashing deep learning performance by leveraging TensorRT.  Back
 
Keywords:
Deep Learning and AI Frameworks, Tools and Libraries, GTC Silicon Valley 2018 - ID S8480
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GPU Accelerated Machine Learning for Bond Price Prediction
Venkat Bala (RN Financial Corporation), Rafael Nicolas Fermin Cota (RN Financial Corporation)
We''ll discuss our application of deep learning and classical machine learning (ML) to the prediction of bond prices. The performance gains obtained from using GPUs over conventional high-performance CPUs for the model training process will be discus ...Read More
We''ll discuss our application of deep learning and classical machine learning (ML) to the prediction of bond prices. The performance gains obtained from using GPUs over conventional high-performance CPUs for the model training process will be discussed.  Back
 
Keywords:
Deep Learning and AI Frameworks, NVIDIA Inception Program, Finance, GTC Silicon Valley 2018 - ID S8655
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Predictive Learning of Factor Based Strategies Using Deep Neural Networks for Investment and Risk Management
Yigal Jhirad (Cohen & Steers), Blay Tarnoff (Cohen & Steers)
We develop and implement an approach using deep neural networks to process financial and macroeconomic signals to help identify key inflection points in equity market-based factor performance such as momentum and volatility. The model may be used to ...Read More
We develop and implement an approach using deep neural networks to process financial and macroeconomic signals to help identify key inflection points in equity market-based factor performance such as momentum and volatility. The model may be used to calibrate factor rotation strategies and better assess portfolio risks associated with factor-based exposures. The machine learning algorithm relies on the GPU for high-performance computations to drive an optimization framework within a deep neural network.  Back
 
Keywords:
Deep Learning and AI Frameworks, Finance, GTC Silicon Valley 2018 - ID S8520
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Myia: A Differentiable Language for Deep Learning
Olivier Breuleux (MILA)
Myia is a new, experimental deep learning framework that aims to provide to deep learning researchers both the expressive power and the performance that they need. Symbolic frameworks such as TensorFlow only cover a curated subset of programming lang ...Read More
Myia is a new, experimental deep learning framework that aims to provide to deep learning researchers both the expressive power and the performance that they need. Symbolic frameworks such as TensorFlow only cover a curated subset of programming language features and do not support second order gradients very well. Dynamic frameworks such as PyTorch, while very powerful, use an operator overloading approach for automatic differentiation, which does not lend itself well to optimization. With Myia, we attempt to have the best of both worlds: we implement a general and composable approach to automatic differentiation over a functional abstraction of a subset of the Python programming language. That subset includes if, while, for, and recursion, providing plenty of expressive power, and yet it can also be analyzed statically to provide the best possible performance. We''ll present the Myia language from a high-level technical perspective, including a short primer on functional programming and automatic differentiation. It is of special interest to deep learning framework or library implementers.  Back
 
Keywords:
Deep Learning and AI Frameworks, Programming Languages, GTC Silicon Valley 2018 - ID S8441
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PyTorch: A Fast and Flexible Deep Learning Framework (Presented by Facebook)
Soumith Chintala (Facebook)
We''ll discuss how to get started with PyTorch from the creator of the project, Soumith Chintala. PyTorch is a fast and flexible deep learning framework that has been called a ''breath of fresh air'' by researchers a ...Read More

We''ll discuss how to get started with PyTorch from the creator of the project, Soumith Chintala. PyTorch is a fast and flexible deep learning framework that has been called a ''breath of fresh air'' by researchers and developers alike for its ease of use, flexibility, and similarity to python programming. It consists of an ndarray library that natively supports GPU execution (automatic differentiation engine that is flexible and fast), and an optimization package for gradient based optimization methods. 

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Keywords:
Deep Learning and AI Frameworks, Deep Learning and AI, GTC Silicon Valley 2018 - ID S8817
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How AI Can Help You Find the Tattoo of Your Dreams
Dennis Micky Jensen (Tattoodo)
Tattoodo, a Copenhagen-based startup, is the No. 1 destination for tattoo lovers around the world, covering all aspects of the global, ever growing tattoo culture. Our platform for the cultivation and appreciation of tattoo art currently has almost 1 ...Read More
Tattoodo, a Copenhagen-based startup, is the No. 1 destination for tattoo lovers around the world, covering all aspects of the global, ever growing tattoo culture. Our platform for the cultivation and appreciation of tattoo art currently has almost 1 million users and among them 50,000 tattoo artists. The platform has over 400,000 uploaded pictures, and renders around 1.5 billion monthly views across all platforms. We spent a lot of time and effort on classifying the tattoo pictures that are uploaded. A community member is able to provide a textual description and tag the tattoo with arbitrary hashtags, which obviously is a lot of responsibility to put in the hands of one member. To build a more useful tattoo platform, we decided to train a convolutional neural network to recognize and classify different tattoo styles. To train it, we used 100,000 images that are already associated with tattoo styles. We are using Caffe, a deep learning framework developed by Berkeley AI Research, which we found suitable for our needs. Training was done on the NVIDIA DIGITS deep learning GPU training system backed by a g2.2xlarge AWS instance.  Back
 
Keywords:
Deep Learning and AI Frameworks, Graphics and AI, GTC Silicon Valley 2018 - ID S8329
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Crash - Practical Applications of Deep Learning in the Insurance Claims Industry
Nigel Cannings (Intelligent Voice)
Deep learning, assisted with GPU acceleration, is pervading many sectors and the insurance space is no exception. We''ll illustrate how deep learning applications in image and speech recognition are forming the backbone of innovative application ...Read More
Deep learning, assisted with GPU acceleration, is pervading many sectors and the insurance space is no exception. We''ll illustrate how deep learning applications in image and speech recognition are forming the backbone of innovative applications in the insurance industry. Real-world examples of image and speech deep learning technology are presented, demonstrating how ground-breaking applications have been engineered in the industry to automate decision-support, assist humans, improve customer experiences and reduce costs.  Back
 
Keywords:
Deep Learning and AI Frameworks, NVIDIA Inception Program, GTC Silicon Valley 2018 - ID S8720
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Aural2: Doing What the User Wants, Before they Finish Speaking (Presented by IBM)
Glen Darling (IBM), Isaac Leonard (IBM)
Many speech based human-machine interaction systems are based on transcribing incoming audio, followed by natural language parsing applied to the resulting list of words. Such word level speech understanding systems have difficulty solving the use-me ...Read More
Many speech based human-machine interaction systems are based on transcribing incoming audio, followed by natural language parsing applied to the resulting list of words. Such word level speech understanding systems have difficulty solving the use-mention distinction; the difference between mentioning a word and using it, a task which humans usually have no difficulty performing. We describe Aural2, an LSTM model and training infrastructure capable of training it to directly transform an audio stream into the user''s intents, slightly before they have finished speaking. This model is small and cheap to run, making it suitable for use on resource constrained edge and IoT devices., and demonstrates the benefits that GPUs bring to edge devices.   Back
 
Keywords:
Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S81037
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Sony's Deep Learning Software. Neural Network Libraries/Console
Yoshiyuki Kobayashi (Sony Corporation)
Neural Network Libraries is latest deep learning framework combining various features such as high speed training and inference with CUDA, ease of use, high portability and high scalability. Neural Network Console is integrated development environmen ...Read More
Neural Network Libraries is latest deep learning framework combining various features such as high speed training and inference with CUDA, ease of use, high portability and high scalability. Neural Network Console is integrated development environment for deep learning that enables full-scale research and development on GUI. These software can be utilized in a wide range of scenes such as for improving productivity of research and development of deep learning, for efficient human resource development, etc. In this session, we will introduce its features and functions according to the actual workflow.  Back
 
Keywords:
Deep Learning and AI Frameworks, Tools and Libraries, GTC Silicon Valley 2018 - ID S8912
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HPE Deep Learning Cookbook: Recipes to Run Deep Learning Workloads
Sergey Serebryakov (Hewlett Packard Labs), Natalia Vassilieva (Hewlett Packard Enterprise)
PE Deep Learning Cookbook is a set of open source tools to guide the choice of the best hardware/software environment for a given deep learning task based on extensive benchmarks of reference deep learning workloads and performance modelling. Deep le ...Read More
PE Deep Learning Cookbook is a set of open source tools to guide the choice of the best hardware/software environment for a given deep learning task based on extensive benchmarks of reference deep learning workloads and performance modelling. Deep learning is a key enabling technology behind the recent revival of artificial intelligence. It is already embedded in different products we use on a daily basis and has the potential to disrupt many industries. There is a vibrant and fast growing ecosystem of software and hardware for deep learning. Various deep learning frameworks are available for anyone who wants to try out this technology. With the variety of choices in hardware configurations and software packages, it is hard to pick the most optimal tools the effectiveness of hardware/software environment varies depending on the deep learning workload. HPE Deep Learning Cookbook is a set of open source tools to guide the choice of the best hardware/software environment for a given deep learning task based on extensive benchmarks of reference deep learning workloads and performance modelling.  Back
 
Keywords:
Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S8555
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Fast Data Pipelines for Deep Learning Training
Trevor Gale (Northeastern University), Simon Layton (NVIDIA), Przemyslaw Tredak (NVIDIA)
With every generation of GPU it becomes increasingly more difficult to keep the data pipeline full so that the GPU can be fully utilized. We'll propose a method for offloading the CPU and using the GPU to process image data to increase thr ...Read More
With every generation of GPU it becomes increasingly more difficult to keep the data pipeline full so that the GPU can be fully utilized. We'll propose a method for offloading the CPU and using the GPU to process image data to increase throughput.  Back
 
Keywords:
Deep Learning and AI Frameworks, Performance Optimization, GTC Silicon Valley 2018 - ID S8906
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AI Embodiment at the Edge: Leveraging Deep Learning with the Intu OSS Project (Presented by IBM)
Glen Darling (IBM), Christopher Dye (IBM)
Augmented Intelligence in human-machine experiences rely on creating presence with users. We call this cognitive embodiment. To achieve this, the AI system must gather intelligence of its spatial and human environment, and possess a semantically rich ...Read More
Augmented Intelligence in human-machine experiences rely on creating presence with users. We call this cognitive embodiment. To achieve this, the AI system must gather intelligence of its spatial and human environment, and possess a semantically rich context of the environment around it. Deep Learning workloads are a valuable element in the composition of cognitive embodiment. Using Intu's 'Self' open source AI Middleware project, on NVIDIA Jetson TX2 GPU-enabled hardware, we present a pattern for AI at the Edge, leveraging insights gathered from local voice recognition, image classification, and video deep learning workloads. A full-bodied AI also reaches out to cloud services for powerful capabilities including Natural Language processing, speech Tonal analysis, extending Embodiment beyond a single device. Self brings all these capabilities to bear for user interaction, face emotion detection, voice command and speech interaction. Have a conversation with an embodied 'Self'.   Back
 
Keywords:
Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S81035
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CuLE : A Companion Library for Accelerated RL Training
Iuri Frosio (NVIDIA)
Traditional RL training is dominated by experience collection processes executing on the CPU. However, this CPU oriented design pattern limits the utility of DL accelerators, such as GPUs. In this talk we present CuLE (cuda learning environment), an ...Read More
Traditional RL training is dominated by experience collection processes executing on the CPU. However, this CPU oriented design pattern limits the utility of DL accelerators, such as GPUs. In this talk we present CuLE (cuda learning environment), an experimental deep RL companion library, to facilitate the generation of RL updates directly on the GPU. CuLE provides an implementation of ALE (atari learning environment), a challenging RL benchmark for discrete episodic tasks, executing directly on the GPU with the number of environments ranging from a few hundred to several thousand. Although traditional deep RL implementations use 12-16 agents coupled with replay memory to achieve training efficiency CuLE can generate a massive number of samples per step and supports new training scenarios that minimize expensive data movement operations. With 1024 agents CuLE achieves an 8-10x performance improvement by executing directly on the GPU compared to 1024 agents running in parallel on a 12-core CPU. We plan to extend CuLE to support a new set GPU-centric deep RL training schemes and new challenging training environments through integration with GFN.?  Back
 
Keywords:
Deep Learning and AI Frameworks, Tools and Libraries, GTC Silicon Valley 2018 - ID S8440
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Finance
Presentation
Media
Deep Thinking: The Challenges of Deep Learning and GPU Acceleration of Financial Data
Erind Brahimi (Wells Fargo)
Accelerated analytics offer massive upside over conventional computing in the financial industry. Deep learning and AI accelerated analytics have many applications in finance, such as fraud detection, risk management, and loss forecasting. GPUs ...Read More
Accelerated analytics offer massive upside over conventional computing in the financial industry. Deep learning and AI accelerated analytics have many applications in finance, such as fraud detection, risk management, and loss forecasting. GPUs are leveraged to provide high-performance computing on a scalable platform for quantitative analysis of big data providing agile methods for ingesting data, performing automated data mining, and implementing robust deep learning architectures. Applying deep learning methods to complex financial data, we can exploit non-linear relationships in financial data that lead to critical risk events.  Back
 
Keywords:
Finance, GTC Silicon Valley 2018 - ID S8754
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Geometric Deep Learning For Long-Term Value Investing
Jonathan Masci (NNAISENSE)
We''ll introduce recent work done by NNAISENSE that harnesses deep learning to automatically build custom portfolios for long-term investing from company fundamentals. NVIDIA GPUs are the cornerstone of our deep learning architecture development, ena ...Read More
We''ll introduce recent work done by NNAISENSE that harnesses deep learning to automatically build custom portfolios for long-term investing from company fundamentals. NVIDIA GPUs are the cornerstone of our deep learning architecture development, enabling the testing of financial models in a walk-forward fashion, where retraining the entire system can be done monthly. The main focus of the talk is on portfolio construction algorithm, which is purely data driven and optimized over criteria such as Sharpe and Information ratio. The central challenge we face is in the design of deep learning systems that can work on sets of observations that are represented in unstructured or structured form (e.g. graphs). We;ll introduce the concepts behind geometric deep learning and show how techniques from this emerging field can help our portfolio construction stage.  Back
 
Keywords:
Finance, NVIDIA Inception Program, GTC Silicon Valley 2018 - ID S8767
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Mergers & Acquisitions using Deep Learning
Jonathan Bailey (EDM Consultancy), Chris Ryan (EDM Consultancy)
We''ll present a case study of how a bank used machine learning to perform due diligence during company acquisitions. Techniques, strategy, and decision making mechanisms that ensured potential risks were illuminated, and mitigated. Technical details ...Read More
We''ll present a case study of how a bank used machine learning to perform due diligence during company acquisitions. Techniques, strategy, and decision making mechanisms that ensured potential risks were illuminated, and mitigated. Technical details on the machine learning briefly discussed. We''ll discuss how to employ cutting edge compute to slash costs and raise your ROI using NVIDIA DGX1 to achieve deep learning in real-time on millions of documents.  Back
 
Keywords:
Finance, NVIDIA Inception Program, GTC Silicon Valley 2018 - ID S8763
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Detection of Financial Statement Fraud using Deep Autoencoder Networks
Timur Sattarov (PricewaterhouseCoopers GmbH WPG), Marco Schreyer (German Research Center for Artificial Intelligence)
Explore how auditors are applying deep learning to detect "anomalous" records in large volumes of accounting data. The Association of Certified Fraud Examiners estimates in its Global Fraud Study 2016 that the typical organization loses 5% ...Read More
Explore how auditors are applying deep learning to detect "anomalous" records in large volumes of accounting data. The Association of Certified Fraud Examiners estimates in its Global Fraud Study 2016 that the typical organization loses 5% of its annual revenues due to fraud. At the same time, organizations accelerate the digitization of business processes affecting Enterprise Resource Planning (ERP) systems. These systems collect vast quantities of electronic journal entry data in general- and sub-ledger accounts at an almost atomic level. To conduct fraud, perpetrators need to deviate from regular system usage or posting pattern. This deviation will be weakly recorded and reflected accordingly by a very limited number of "anomalous" journal entries of an organization. To anomalous journal entries, several deep auto-encoder networks are trained using NVIDIA''s DGX-1 system. The empirical evaluation on two real-world accounting datasets underpinned the effectiveness of the trained network in capturing journal entries highly relevant for a detailed audit while outperforming several baseline methods.  Back
 
Keywords:
Finance, Finance - Quantitate Risk Management, GTC Silicon Valley 2018 - ID S8343
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Graphics and AI
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Quadro for AI, VR and Simulation (Presented by PNY Technologies)
Carl Flygare (PNY Technologies)
Quadro has always been the "Gold Standard" for professional graphics, but it also runs the latest CUDA implementation and is compatible with essential NVIDIA APIs, developer tools and frameworks. As deep learning and AI bring new ways of do ...Read More
Quadro has always been the "Gold Standard" for professional graphics, but it also runs the latest CUDA implementation and is compatible with essential NVIDIA APIs, developer tools and frameworks. As deep learning and AI bring new ways of doing to Quadro, capabilities like virtual and augmented reality catalyze new ways of seeing and solving problems with Quadro across industry sectors ranging from AEC, Energy, Manufacturing, Media & Entertainment, Healthcare, Government and Defense, to Scientific and Technical computing. Some Quadro products are even optimized for AI development and constitute a de facto nexus where Deep Learning and AI, GPGPU accelerated simulation, physically-based photorealistic rendering, and the latest VR techniques come together to offer dramatically differentiated solutions - making Quadro a critical component of every GPU development and deployment discussion - and an core tool in the NVIDIA portfolio of GPU-enabled solutions. This session details why Quadro remains a vibrant and essential component of NVIDIA's ecosystem.  Back
 
Keywords:
Graphics and AI, Virtual Reality and Augmented Reality, GTC Silicon Valley 2018 - ID S8901
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HPC and AI
Presentation
Media
How GPU Server Architectures Deliver increase productivity for Deep Learning Training Workloads & HPC Customers (Presented by Supermicro)
Sarosh Irani (Supermicro), Jason Pai (Super Micro Computer, Inc.)
Overview of numerous GPU hardware platforms designed for today's taxing AI/machine learning and HPC workloads, including custom solutions targeted for Deep Learning Inferencing and Deep Learning Training. Talk will cover systems based on PCIe based ...Read More
Overview of numerous GPU hardware platforms designed for today's taxing AI/machine learning and HPC workloads, including custom solutions targeted for Deep Learning Inferencing and Deep Learning Training. Talk will cover systems based on PCIe based GPUs as well as GPU systems with the NVLink interface.  Back
 
Keywords:
HPC and AI, GTC Silicon Valley 2018 - ID S8999
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Enhancing Experimental Design and Understanding with Deep Learning/AI
Vic Castillo (Lawrence Livermore National Lab)
Computer simulations offer great insight into complex, dynamical systems but can be difficult to navigate through a large set of control/design parameters. Deep learning methods, applied on fast GPUs, can provide an ideal way to improve scientific an ...Read More
Computer simulations offer great insight into complex, dynamical systems but can be difficult to navigate through a large set of control/design parameters. Deep learning methods, applied on fast GPUs, can provide an ideal way to improve scientific and engineering workflows. In this talk, Vic will discuss an application of machine learning to develop a fast-running surrogate model that captures the dynamics of an industrial multiphase fluid flow. He will also discuss an improved population search method that can help the analyst explore a high-dimensional parameter space to optimize production while reducing the model uncertainty.  Back
 
Keywords:
HPC and AI, GTC Silicon Valley 2018 - ID S8828
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Optimizing Distributed GPU Collectives for Deep Learning Workloads
Pidad Gasfar D'Souza (IBM Systems Development Lab)
In this session we present MPI collective algorithms optimized for distributed deep learning frameworks. The performance of large message MPI collectives such as broadcast, allreduce, reduce etc. are critical to the performance of these workloads. Th ...Read More
In this session we present MPI collective algorithms optimized for distributed deep learning frameworks. The performance of large message MPI collectives such as broadcast, allreduce, reduce etc. are critical to the performance of these workloads. There is a need for a novel approach towards the design of large scale collective communication algorithms for CUDA aware MPI runtimes. The session will deep dive into our implementation of these collectives and its performance advantages on IBM POWER 9 Systems with NVIDIA V100 GPUs for OSU benchmark and Distributed TensorFlow.  Back
 
Keywords:
HPC and AI, Performance Optimization, GTC Silicon Valley 2018 - ID S8306
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Towards Scalable Parallel Training of Deep Neural Networks
Brian Van Essen (Lawrence Livermore National Lab)
We propose a new framework for parallelizing deep neural network training that maximizes the amount of data that is ingested by the training algorithm. Our proposed framework called Livermore Tournament Fast Batch Learning (LTFB) targets large-scale ...Read More
We propose a new framework for parallelizing deep neural network training that maximizes the amount of data that is ingested by the training algorithm. Our proposed framework called Livermore Tournament Fast Batch Learning (LTFB) targets large-scale data problems. The LTFB approach creates a set of Deep Neural Network (DNN) models and trains each instance of these models independently and in parallel. Periodically, each model selects another model to pair with, exchanges models, and then run a local tournament against held-out tournament datasets. The winning model continues training on the local training datasets. This new approach maximizes computation and minimizes amount of synchronization required in training deep neural network, a major bottleneck in existing synchronous deep learning algorithms. We evaluate our proposed algorithm on two HPC machines at Lawrence Livermore National Laboratory including an early access IBM Power8+ with NVIDIA Tesla P100 GPUs machine. Experimental evaluations of the LTFB framework on two popular image classification benchmark: CIFAR10 and ImageNet, show significant speed up compared to the sequential baseline.  Back
 
Keywords:
HPC and AI, HPC and Supercomputing, GTC Silicon Valley 2018 - ID S8829
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Designing and Deploying End to End HPC and AI Solutions: Lessons learned from large scale HPC and AI Clusters (Presented by Penguin Computing)
Kevin Tubbs (Penguin Computing)
We will discuss challenges and lessons learned from deploying multiple large scale HPC and AI clusters in different industries. Lessons learned will focus on end-to-end aspects of designing and deploying large scale gpu clusters including datacenter ...Read More
We will discuss challenges and lessons learned from deploying multiple large scale HPC and AI clusters in different industries. Lessons learned will focus on end-to-end aspects of designing and deploying large scale gpu clusters including datacenter and environmental challenges, network performance and optimization, data pipeline and storage challenges as well as workload orchestration and optimization. You will learn more about open architectures for HPC, AI and Deep Learning, combining flexible compute architectures, rack scale platforms, and software-defined networking and storage, to provide a scalable software-defined deep learning environment. We will discuss strategies, providing insight into everything from specialty compute for training vs. inference to high performance storage for data workflows to orchestration and workflow management tools. We will also discuss deploying deep learning environments from development to production at scale from private cloud to public cloud.  Back
 
Keywords:
HPC and AI, GTC Silicon Valley 2018 - ID S8972
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ORNL Summit: Scaling Deep Learning for Scientific Workloads on Summit
Jack Wells (Oak Ridge National Laboratory)
HPC centers have been traditionally configured for simulation workloads, but deep learning has been increasingly applied alongside simulation on scientific datasets. These frameworks do not always fit well with job schedulers, large parallel file sys ...Read More
HPC centers have been traditionally configured for simulation workloads, but deep learning has been increasingly applied alongside simulation on scientific datasets. These frameworks do not always fit well with job schedulers, large parallel file systems, and MPI backends. We'll discuss examples of how deep learning workflows are being deployed on next-generation systems at the Oak Ridge Leadership Computing Facility. We'll share benchmarks between native compiled versus containers on Power systems, like Summit, as well as best practices for deploying learning and models on HPC resources on scientific workflows.  Back
 
Keywords:
HPC and AI, HPC and Supercomputing, GTC Silicon Valley 2018 - ID S8551
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Industrial Inspection
Presentation
Media
Deep Learning with Sparse Uncertain Data: An Oil & Gas Perspective
Michael Kennedy (Baker Hughes, a GE company), Arun Subramaniyan (Baker Hughes, a GE Company)
Deep learning techniques have the potential to enable a step change in modeling efficiency for industrial systems. By increasing efficiency and accuracy of diagnostics, and extracting meaning from large amounts of industrial data, deep learning provi ...Read More
Deep learning techniques have the potential to enable a step change in modeling efficiency for industrial systems. By increasing efficiency and accuracy of diagnostics, and extracting meaning from large amounts of industrial data, deep learning provides a pathway to truly differentiated outcomes. In this talk, we will discuss our experience building deep learning models for Oil & Gas applications and the CI/CD process for managing the lifecycle of the models in production. We will present novel applications of deep learning for anomaly detection, rock formation identification and optimization. The hybrid modeling framework combining physics-based models with deep learning techniques will be highlighted with specific application of production optimization.  Back
 
Keywords:
Industrial Inspection, GTC Silicon Valley 2018 - ID S8789
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Intelligent Video Analytics and Smart Cities
Presentation
Media
Building Smarter Cities with AI-Powered Applications
Andrew Herson (Verizon)
Learn how Verizon is helping create safer streets, reducing traffic congestion, aiding the navigation of both vehicles and pedestrians, and reducing energy costs and consumption through AI-enabled sensor based networks that leverage LED street l ...Read More

Learn how Verizon is helping create safer streets, reducing traffic congestion, aiding the navigation of both vehicles and pedestrians, and reducing energy costs and consumption through AI-enabled sensor based networks that leverage LED street lighting infrastructure. We will discuss our Vision Zero application and how use deep learning to recognize, detect, classify and concurrently track vehicles in traffic, pedestrians, bicyclists, and parked cars, and turn it into actionable data to help make better urban planning decisions and quantify the results.

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Keywords:
Intelligent Video Analytics and Smart Cities, Autonomous Machines, GTC Silicon Valley 2018 - ID S8966
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Introduction to Deep Stream SDK
Kaustubh Purandare (NVIDIA)
Introduction to high performance deep learning inference for video analytics. NVIDIA DeepStreamSDK simplifies the development of scalable intelligent video analytics (IVA) applications powered by deep learning for smart cities and hyperscale datacent ...Read More
Introduction to high performance deep learning inference for video analytics. NVIDIA DeepStreamSDK simplifies the development of scalable intelligent video analytics (IVA) applications powered by deep learning for smart cities and hyperscale datacenters.   Back
 
Keywords:
Intelligent Video Analytics and Smart Cities, GTC Silicon Valley 2018 - ID S81047
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Improving Commercial Fleet Safety and Performing High-Def Mapping at the Same Time
David Julian (Netradyne)
In this talk will discuss how deploying cameras, sensors, and deep learning in commercial vehicles accelerated by NVIDIA Jetson can help analyze the driving environment in real time, improve driver safety, while at the same time performing dynamic HD ...Read More
In this talk will discuss how deploying cameras, sensors, and deep learning in commercial vehicles accelerated by NVIDIA Jetson can help analyze the driving environment in real time, improve driver safety, while at the same time performing dynamic HD mapping.  Back
 
Keywords:
Intelligent Video Analytics and Smart Cities, GTC Silicon Valley 2018 - ID S8856
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Inventory
Presentation
Media
CPG Product Capture Under 48 hours - From Production Lines to Retail Shelves
Pradeep Pydah (Maxerience)
New Consumer Packaged Goods(CPG) & products need not just sit in retail shelves without having a link back to their production lines.Our solutions, through a patent pending process, create a feedback link between retail shelves and manufacturin ...Read More
New Consumer Packaged Goods(CPG) & products need not just sit in retail shelves without having a link back to their production lines.Our solutions, through a patent pending process, create a feedback link between retail shelves and manufacturing entities, production lines that manufacture CPG products and distribution channels - to enable processes that solve out-of-stock issues in retail shelves and also understand customer behavior at the shelf level.Evolving from a solution that was intended to solve out-of-stock issues in retail shelves in real time, our current generic CPG product training platform is set to create a global database of CPG products with their respective descriptions including ingredients,nutrition etc. In this session, we will walk you through how an ecosystem was built, with deep learning at it's core. You will get insights on how GPU's have helped in speeding up the creation of the ecosystem. The session ends with what the future of retail holds in terms of maximizing the human experience through empathy and responsibility -Nested distribution networks for redistribution of unsold retail shelf food in low income groups -enabled by AI.  Back
 
Keywords:
Inventory, NVIDIA Inception Program, GTC Silicon Valley 2018 - ID S8710
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Medical Imaging and Radiology
Presentation
Media
From Bits to Bedside: Translating Large-Scale Routine Clinical Datasets into Precision Mammography
Dexter Hadley (UCSF)
We'll demonstrate how to use deep learning (DL) approaches to translate big data from routine clinical care into medical innovation that directly improves routine clinical care. Typically, large healthcare institutions have sufficient quantities of ...Read More
We'll demonstrate how to use deep learning (DL) approaches to translate big data from routine clinical care into medical innovation that directly improves routine clinical care. Typically, large healthcare institutions have sufficient quantities of clinical data to facilitate precision medicine through a DL paradigm. However, this clinical data is hardly translated into direct clinical innovation because computer algorithms cannot readily ingest or reason over it. Using routine mammographic screening data for breast cancer as an example, we first downloaded over 30,000 free text pathology reports and used long short term memory DL algorithms to infer cancer outcomes for individual patients. We then labeled over 700,000 mammographic views of breast imaging with our inferred pathology outcomes. Finally, we trained convolutional neural network DL algorithms to directly predict pathology outcomes from breast imaging. With our approach, we demonstrate how to leverage DL to realize precision oncology and significantly improve the interpretation of routine screening mammography for millions of women using routine clinical big data.  Back
 
Keywords:
Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8471
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Performance Optimization
Presentation
Media
Everything You Need to Know About Unified Memory
Nikolay Sakharnykh (NVIDIA)
We'll cover all the things you need to know about Unified Memory: fundamental principles, important use cases, advances in the latest GPU architectures, HMM and ATS details, performance considerations and optimization ideas, and new application resu ...Read More
We'll cover all the things you need to know about Unified Memory: fundamental principles, important use cases, advances in the latest GPU architectures, HMM and ATS details, performance considerations and optimization ideas, and new application results, including data analytics and deep learning. 2018 is going to be the year of Unified Memory. Both HMM and ATS will be available and developers will start using the true Unified Memory model with the system allocator "the way it's meant to be played." We'll discuss all the caveats and differences between cudaMallocManaged and malloc. A big part of the talk will be related to performance aspects of Unified Memory: from migration throughput optimizations to improving the overlap between kernels and prefetches.  Back
 
Keywords:
Performance Optimization, Programming Languages, HPC and Supercomputing, GTC Silicon Valley 2018 - ID S8430
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Predictive Analytics for Retail
Presentation
Media
How Comcast Uses Deep Learning to Build Intelligent Products and Applications
Jan Neumann (Comcast)
We'll describe how Comcast uses GPU-powered machine learning to build intelligent products specifically focusing on the areas of smart home video analytics and virtual assistants for customer service. We'll explain how we use deep learning to alert ...Read More
We'll describe how Comcast uses GPU-powered machine learning to build intelligent products specifically focusing on the areas of smart home video analytics and virtual assistants for customer service. We'll explain how we use deep learning to alert our customers of noteworthy events being observed by their smart home cameras, and how it helps us to accurately understand the intent of our customers when they contact us via our virtual assistants and how we use reinforcement learning to identify how to best resolve their concerns. We'll also talk about how our distributed multi-GPU clusters speed up training the models and enable inference at Comcast scale.  Back
 
Keywords:
Predictive Analytics for Retail, Telecom Industry Solutions, Speech and Language Processing, GTC Silicon Valley 2018 - ID S8259
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Programming Languages
Presentation
Media
Portable Designs for Performance Using the Hybrid Task Graph Scheduler
Timothy Blattner (National Institute of Standards and Technology)
Adding GPUs to a compute node greatly expands its computational capacity. However, taking advantage of such nodes is challenging. This talk presents the Hybrid Task Graph Scheduler (HTGS), an abstract execution model and framework, which simplifies d ...Read More
Adding GPUs to a compute node greatly expands its computational capacity. However, taking advantage of such nodes is challenging. This talk presents the Hybrid Task Graph Scheduler (HTGS), an abstract execution model and framework, which simplifies developing applications for multi-GPU nodes by modularizing a program into compute kernels, memory management, data motion, and state maintenance. Furthermore, HTGS maintains a task graph representation at runtime and collects task-level profile data, thereby identifying bottlenecks and supporting experimentation for performance. We will present imaging applications that use HTGS to process and analyze gigapixel images with Deep Learning. We will also present two linear algebra benchmarks that exhibits the applicability of HTGS beyond imaging.  Back
 
Keywords:
Programming Languages, Tools and Libraries, GTC Silicon Valley 2018 - ID S8634
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Robotics & Autonomous Machines
Presentation
Media
Real-Time Computer Vision and Deep Learning with Open-Source Software on an Open-Hardware Jetson-Based Camera
Morgan Quigley (Open Robotics)
We'll present our latest work in embedding open-source software inside the camera itself. More specifically, we'll show an open hardware design that includes a stereo imager pair, an FPGA, and a Jetson TX2 CPU/GPU running several open-source comput ...Read More
We'll present our latest work in embedding open-source software inside the camera itself. More specifically, we'll show an open hardware design that includes a stereo imager pair, an FPGA, and a Jetson TX2 CPU/GPU running several open-source computer vision libraries and the robot operating system (ROS) as a middleware to connect the computer vision software to the outside world. We'll also demonstrate the security features of the latest version of ROS 2, which can protect the potentially sensitive imagery and calculated vision data from evildoers with access to network traffic.  Back
 
Keywords:
Robotics & Autonomous Machines, IoT, Robotics & Drones, Computer Vision, GTC Silicon Valley 2018 - ID S8810
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AI at the Edge - Intelligent Machines
Jesse Clayton (NVIDIA)
Artificial intelligence is impacting almost every part of the industrial and agricultural supply chain. From robots that quickly adapt to build new products, to automated vehicles that address last-mile challenges for product delivery, to UAVs t ...Read More

Artificial intelligence is impacting almost every part of the industrial and agricultural supply chain. From robots that quickly adapt to build new products, to automated vehicles that address last-mile challenges for product delivery, to UAVs that can automatically detect failing infrastructure, the world is transitioning from processes that are largely manual to ones that are largely automated. We'll discuss how AI and deep learning are enabling these advances. We'll also analyze a sampling of early successes across different applications. And finally we'll describe some of the remaining challenges to wide-scale deployment, and the work NVIDIA is doing to address those challenges via its Isaac initiative.

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Keywords:
Robotics & Autonomous Machines, IoT, Robotics & Drones, NVIDIA Inception Program, Autonomous Machines, GTC Silicon Valley 2018 - ID S8915
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Automating the Last Mile
Kevin Peterson (Marble)
Self-driving vehicles will transform every aspect of how we work and play. Humanity spends 500 million hours each day driving to and from the grocery store. The impact of automating these tasks is huge. Marble is building self-driving delivery vehicl ...Read More
Self-driving vehicles will transform every aspect of how we work and play. Humanity spends 500 million hours each day driving to and from the grocery store. The impact of automating these tasks is huge. Marble is building self-driving delivery vehicles to give you back this time and make delivery a delightful experience. I'll talk about why delivery is a good application of robotics, and how deep learning enables us to automate driving.  Back
 
Keywords:
Robotics & Autonomous Machines, IoT, Robotics & Drones, NVIDIA Inception Program, GTC Silicon Valley 2018 - ID S8914
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Generalizable Autonomy for Robotic Mobility and Manipulation
Animesh Garg (Stanford University), Marynel Vázquez (Stanford University)
Understanding the link between perception and action is key to building autonomous agents that can perform challenging tasks in unstructured environments among humans. The Stanford Vision & Learning Lab works at the interface of vision, language ...Read More
Understanding the link between perception and action is key to building autonomous agents that can perform challenging tasks in unstructured environments among humans. The Stanford Vision & Learning Lab works at the interface of vision, language and robotics and, in this talk, we will discuss recent advances with deep learning in both mobility and manipulation. We will talk about our mobile experimental platform, JackRabbot, which is equipped with an on-board GPU to perform visual tasks in real time, and discuss topics related to human motion understanding. We will also talk about robot autonomy, requiring both understanding of perceptual inputs and reasoning at different levels of abstractions. We will present new approaches to imitation learning for robot manipulation, including Neural Task Programming. This new approach to meta-learning capitalizes on hierarchy and learns to "program" with a modular Robot API to perform unseen tasks with a single test example. Finally, we will discuss task structure learning as an intermediate step towards imitation from videos for complex tasks.  Back
 
Keywords:
Robotics & Autonomous Machines, IoT, Robotics & Drones, GTC Silicon Valley 2018 - ID S8927
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Deep Neural Network-Based Cooperative Visual Tracking Through Multiple Flying Robots
Aamir Ahmad (Max Planck Institute for Intelligent Systems), Eric Price (Max Planck Institute for Intelligent Systems)
Human and animal full-body motion capture (MoCap) in outdoor scenarios is a challenging and largely unsolved problem. We''ll introduce a multiple flying robots-based solution for it. MoCap systems like Vicon, Optitrack, and the 4D Dynamic Body Scanne ...Read More
Human and animal full-body motion capture (MoCap) in outdoor scenarios is a challenging and largely unsolved problem. We''ll introduce a multiple flying robots-based solution for it. MoCap systems like Vicon, Optitrack, and the 4D Dynamic Body Scanner at MPI-IS Tuebingen achieve high degrees of accuracy in indoor settings. Besides being bulky, they make use of reflected infrared light and heavily rely on precisely calibrated wall or ceiling-mounted fixed cameras. Consequently, such systems cannot be used to perform MoCap in outdoor scenarios where changing ambient light conditions persist and permanent fixtures in the environment cannot be made. Our outdoor MoCap solution involves flying robots with on-board cameras, Intel i7 CPUs, NVIDIA Jetson TX1 GPU modules, and a deep learning-based approach.  Back
 
Keywords:
Robotics & Autonomous Machines, IoT, Robotics & Drones, Computer Vision, GTC Silicon Valley 2018 - ID S8796
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Real Time and Dynamic Risk Assessments for Autonomous Vehicles
Sunil Chintakindi (Allstate)
Incorporating high fidelity or HD map data and real time traffic data such as speeds and congestion patterns into risk assessments, particularly for ADAS and highly autonomous vehicle operation, is mostly uncharted territory. We'll explore this doma ...Read More
Incorporating high fidelity or HD map data and real time traffic data such as speeds and congestion patterns into risk assessments, particularly for ADAS and highly autonomous vehicle operation, is mostly uncharted territory. We'll explore this domain by deploying data tools and techniques that are the intersection of automotive, deep learning, and insurance industries.  Back
 
Keywords:
Robotics & Autonomous Machines, IoT, Robotics & Drones, Finance, GTC Silicon Valley 2018 - ID S8778
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Enabling Future Mobility Solutions with Automatic Vehicle Inspection Using Deep Learning
Amir Hever (UVeye)
In recent times, there have been many advances in anomaly detection for computer vision applications. Despite this, the problem of  anomaly detection on any vehicles undercarriage remains very challenging for two main reasons:  First, the d ...Read More
In recent times, there have been many advances in anomaly detection for computer vision applications. Despite this, the problem of  anomaly detection on any vehicles undercarriage remains very challenging for two main reasons:  First, the data domain for a vehicle undercarriage is very unique; there is no publicly available data set, and it's not readily available online. Second, there is no dataset of threats to be detected, which can appear in any place or form (weapons, contraband etc). Essentially, this is a semi-supervised anomaly detection problem, where the anomaly class does not exist in the dataset. In this presentation, we will describe the steps we took to solve this problem, including deep learning models for representations of vehicles, similarity metrics, segmentation, anomaly detection, and how all these models are combined into a singular system that analyzes a vehicle in just a few seconds. We will also show how models trained for security purpose have great value in the automotive industry, whereby in using similar systems we can detect various types of mechanical problems and damages to the exterior of any vehicle.   Back
 
Keywords:
Robotics & Autonomous Machines, IoT, Robotics & Drones, GTC Silicon Valley 2018 - ID S81031
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How to Win the Amazon Robotics Challenge with Deep Learning and Robotic Vision
Juxi Leitner (Australian Centre for Robotic Vision), Doug Morrison (Australian Centre for Robotic Vision)
The Amazon Robotics Challenge had 16 teams compete at the 2017 Amazon Robotics Challenge global finals in Nagoya, Japan. Each team was challenged to design a pick-and-place robot for autonomous warehousing to address the need for development in robot ...Read More
The Amazon Robotics Challenge had 16 teams compete at the 2017 Amazon Robotics Challenge global finals in Nagoya, Japan. Each team was challenged to design a pick-and-place robot for autonomous warehousing to address the need for development in robotic vision and manipulation. We'll present Cartman, our custom-built, cost-effective robot system, which won first place in the competition finals by stowing 14 (out of 16) and picking all nine items in 27 minutes. We'll highlight our experience-centered design methodology and key aspects of our system that contributed to our competitiveness. In particular, for a perception system we built a deep learned semantic segmentation network, which was trained on only about a dozen images per previously unseen item. By conducting training on four GeForce GTX 1080 GPUs, we created an effective robot system with robust perception that was tightly integrated with our hardware and critical to our win.  Back
 
Keywords:
Robotics & Autonomous Machines, IoT, Robotics & Drones, GTC Silicon Valley 2018 - ID S8842
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The Future of Swiss Railway Dispatching: Deep Learning and Simulation on DGX-1
Adrian Egli (Swiss Federal Railway Company SBB), Erik Nygren (Swiss Federal Railway Company SBB)
We'll highlight the benefits of using GPU accelerated high performance simulations on DGX systems in combination with deep reinforcement learning. Deep reinforcement learning has gained a lot of momentum lately with its success in solving various co ...Read More
We'll highlight the benefits of using GPU accelerated high performance simulations on DGX systems in combination with deep reinforcement learning. Deep reinforcement learning has gained a lot of momentum lately with its success in solving various computer games and control problems. Based on these promising results we pursue the approach of optimizing train schedules and train dispatching with deep reinforcement learning. We implemented GPU accelerated high performance train network simulations to allow us to train multiple realizations of the dispatching agent in parallel at super real-time speed and react to the ever-changing topology and traffic situation. The result is that we are able to perform training in a feasible time span and explore novel dispatching and scheduling strategies for current and future railway traffic. Key words: Deep Learning and AI, DGX  Back
 
Keywords:
Robotics & Autonomous Machines, IoT, Robotics & Drones, Inventory, GTC Silicon Valley 2018 - ID S8184
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Speech and Language Processing
Presentation
Media
Achieving Human Parity in Conversational Speech Recognition Using CNTK and a GPU Farm
Frank Seide (Microsoft), Andreas Stolcke (Microsoft)
Microsoft's speech recognition research system has recently achieved a milestone by matching professional human transcribers in how accurately it transcribes natural conversations, as measured by government benchmark tasks. In this talk we will dis ...Read More
Microsoft's speech recognition research system has recently achieved a milestone by matching professional human transcribers in how accurately it transcribes natural conversations, as measured by government benchmark tasks. In this talk we will discuss the significance of the result, give a high-level overview of the deep learning and other machine learning techniques used, and detail the software techniques used. A key enabling factor was the use of CNTK, the Microsoft Cognitive Toolkit, which allowed us to train hundreds of acoustic models during development, using a farm of GPU servers and parallelized training. Model training was parallelized on GPU host machines, using 1-bit distributed stochastic gradient descent algorithm. LSTM acoustic and language model training takes advantage of CNTK's optimizations for recurrent models, such as operation fusion, dynamic unrolling, and automatic packing and padding of variable length sequences. We also give an overview of CNTK's functional API.  Back
 
Keywords:
Speech and Language Processing, GTC Silicon Valley 2018 - ID S8576
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Virtual Reality and Augmented Reality
Presentation
Media
Revolutionizing Virtual Production with VR and Deep Learning
Michael Ford (Sony Pictures Imageworks), Richard Grandy (NVIDIA), Ben Grossmann (Magnopus), Darren Hendler (Digital Domain), Rev Lebaredian (NVIDIA), Lap Luu (Magnopus), John Root (Technicolor)
Virtual Production is revolutionizing the way the world creates cinematic and immersive content. Advances in virtual reality(VR) and deep learning (DL) are bring new capabilities to storytellers enabling them to interactively design new worlds, ...Read More

Virtual Production is revolutionizing the way the world creates cinematic and immersive content. Advances in virtual reality(VR) and deep learning (DL) are bring new capabilities to storytellers enabling them to interactively design new worlds, animate lifelike characters, and visualize complex scenes all created, edited, and reviewed in real time. This panel will look into the future to explore the process, tools, and workflows made possible by Nvidia's advances in artificial intelligence, real time graphics, and high performance computing. Panelists will dive into the challenges this paradigm shift brings to on-set production as well as the potential for greater efficiency and enhanced exploration.

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Keywords:
Virtual Reality and Augmented Reality, GTC Silicon Valley 2018 - ID S8935
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AI + VR: Exploring the Intersection of Two Revolutions
As a new computing paradigm, Virtual Reality (VR) is changing workflows and redefining how we interact with computers. Deep Learning (DL) is revolutionizing business processes, defining how Robotics & Autonomous Machines interact with us and ...Read More

As a new computing paradigm, Virtual Reality (VR) is changing workflows and redefining how we interact with computers. Deep Learning (DL) is revolutionizing business processes, defining how Robotics & Autonomous Machines interact with us and with the world, and demanding applicant developers learn new ways of working in every field touching compute. In this panel we explore the intersection of these two revolutions with VR industry innovators who are leveraging deep learning using NVIDIA GPU compute systems to bring depth to the VR experience. This discussion will focus on the use of Artificial Intelligence (AI) in both building rich VR environments and enhancing the user's interaction with the VR environment. Panelists will share their vision on how AI will shape the near future of VR and give the audience a view of potential challenges to that future. The panelists will explore: o Pain points in creating VR experiences, which are driving adoption of AI in the VR space o Challenges encountered in using DL to bring rich content to life in a VR environment o Challenges of implementing DL-enhanced VR environment interaction within the latency-critical VR space o How DL/AI will continue to fundamentally change the VR space

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Keywords:
Virtual Reality and Augmented Reality, GTC Silicon Valley 2018 - ID S8931
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