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GTC ON-DEMAND

Artificial Intelligence and Deep Learning
Presentation
Media
Abstract:
NVIDIA에서 전 세계에 있는 솔루션 아키텍처와 엔지니어링팀을 이끌고 있는 Marc Hamilton은 글로벌 고객과 파트너에게 인공지능, 딥 러닝, 프로페셔널 비주얼라이제이션, 그리고 고성능 컴퓨팅을 위한 세계 최고의 엔드 투 앤드(End-to-end) 솔루션을 제공하고 있습니다. 이번 키노트에서 가장 최신의 AI 기술 트렌드와 현재 NVIDIA가 어떻게 다양한 산업군에 걸쳐 AI 혁신을 가져오고 있는지 소개합니다 ...Read More
Abstract:
NVIDIA에서 전 세계에 있는 솔루션 아키텍처와 엔지니어링팀을 이끌고 있는 Marc Hamilton은 글로벌 고객과 파트너에게 인공지능, 딥 러닝, 프로페셔널 비주얼라이제이션, 그리고 고성능 컴퓨팅을 위한 세계 최고의 엔드 투 앤드(End-to-end) 솔루션을 제공하고 있습니다. 이번 키노트에서 가장 최신의 AI 기술 트렌드와 현재 NVIDIA가 어떻게 다양한 산업군에 걸쳐 AI 혁신을 가져오고 있는지 소개합니다  Back
 
Topics:
Artificial Intelligence and Deep Learning
Type:
Keynote
Event:
AI Conference Korea
Year:
2019
Session ID:
SKR9101
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Deep Learning & AI Frameworks
Presentation
Media
Opening Keynote (Keynote Talk)
Abstract:
Don't miss this keynote from NVIDIA Founder & CEO, Jensen Huang, as he speaks on the future of computing.
 
Topics:
Deep Learning & AI Frameworks, Intelligent Machines, IoT & Robotics, Autonomous Vehicles, Data Center & Cloud Infrastructure
Type:
Keynote
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9688
Streaming:
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5G & Edge
Presentation
Media
Abstract:
Significant breakthrough in 5G has evolved many IoT applications in various fields including business, manufacturing, health care and transportation. The evolution of GPU is the key enabler to the enriched applications by leveraging the power of AI @ ...Read More
Abstract:
Significant breakthrough in 5G has evolved many IoT applications in various fields including business, manufacturing, health care and transportation. The evolution of GPU is the key enabler to the enriched applications by leveraging the power of AI @ the Edge. Edge computing still leverages the cloud as a crucial part of the system and many applications will harness the power of 5G features such as high speeds multi-gigabit connections, huge amounts of data bandwidth, unprecedented amounts of capacity, super-low latency and ultra-reliable low latency communications (URLLC). This session will explore the opportunities of some of the interesting applications to help our community and environment.  Back
 
Topics:
5G & Edge
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91037
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Abstract:
We'll examine latency and performance challenges involved in deploying deep learning technologies that improve voice quality in real-time communications. We'll explain how deep learning changes traditional voice enhancement (e.g. noise cancellation ...Read More
Abstract:
We'll examine latency and performance challenges involved in deploying deep learning technologies that improve voice quality in real-time communications. We'll explain how deep learning changes traditional voice enhancement (e.g. noise cancellation), and cover our work using deep learning to eliminate the need for multiple microphones, which enforce a form factor such as a phone or headset. We'll show how moving those processes to software offers the flexibility to deploy the technology on headsets, mobile, laptops, and in the network. We will describe how we power and scale our DL-based algorithm on GPUs, which scale up to 100 times better than CPUs for server-side processing. We'll also discuss how we used CUDA and TensorRT to fit within the constraint of 12ms latency on an end-to-end real-time call.  Back
 
Topics:
5G & Edge, AI Application, Deployment & Inference, Speech & Language Processing, Performance Optimization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9222
Streaming:
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Abstract:
Learn how NVIDIA's GPUs are used to accelerate unified communications (UC) analytics processing by mathematically classifying UC call flows. We'll discuss how we're leveraging NVIDIA GPU parallelization technology to support classification and bas ...Read More
Abstract:
Learn how NVIDIA's GPUs are used to accelerate unified communications (UC) analytics processing by mathematically classifying UC call flows. We'll discuss how we're leveraging NVIDIA GPU parallelization technology to support classification and baselining of UC call flows, protect UC against fraudulent attacks, and establish predictive UC forecasting models. We'll explain how this allows us to more accurately identify and forecast deviations that may represent malicious use of UC against a baseline of normal traffic.  Back
 
Topics:
5G & Edge
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9385
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Abstract:
We'll explore the application of generative modeling approaches to learning wireless network data distribution for network simulation and performance optimization. We'll compare three machine learning models that can be used for this purpose Gaussi ...Read More
Abstract:
We'll explore the application of generative modeling approaches to learning wireless network data distribution for network simulation and performance optimization. We'll compare three machine learning models that can be used for this purpose Gaussian mixture models, kernel density estimation, and generative adversarial networks.  Back
 
Topics:
5G & Edge
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9680
Streaming:
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Abstract:
Learn how to accelerate deep learning-based image recognition applications on the 5G network, the next-generation cellular mobile network. The 5G network will enable low latency and high data-rate telecommunication, making it suitable for deep learni ...Read More
Abstract:
Learn how to accelerate deep learning-based image recognition applications on the 5G network, the next-generation cellular mobile network. The 5G network will enable low latency and high data-rate telecommunication, making it suitable for deep learning applications that need to post and get large amounts of data via the network or need real-time inference. We'll discuss how we're working to use these 5G characteristics by developing image- and video-recognition services on the mobile phone network. These include surveillance-camera recognition, adaptive digital signage, and image recognition for retail companies. In addition, we'll explain how to make telecommunication between the edge application and the cloud resource more secure and more efficient for deep learning applications.  Back
 
Topics:
5G & Edge, AI & Deep Learning Research, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9718
Streaming:
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Abstract:
Many applications like telemetry, intrusion detection and anomaly detection can be accelerated by doing Packet Processing on a GPU. Using a GPU will also enable applying ML/DL for smarter Packet Processing. However, one of the bottlenecks to do Packe ...Read More
Abstract:
Many applications like telemetry, intrusion detection and anomaly detection can be accelerated by doing Packet Processing on a GPU. Using a GPU will also enable applying ML/DL for smarter Packet Processing. However, one of the bottlenecks to do Packet Processing on a GPU is to be able to ingest data at high bandwidth and low latency. The recent developments in this field will be reviewed in this session. Following this, the latest development from Nvidia along with preliminary benchmarking results will be presented. Nvidia has extended DPDK library to leverage direct RDMA to GPU memory, enabling close to line rate ingestion of network data into a GPU on 100GigE networks.  Back
 
Topics:
5G & Edge, Data Center & Cloud Infrastructure
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9730
Streaming:
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Abstract:
We'll examine what the evolution of the 5G network means for telecommunications providers and examine how supporting the jump to 5G will require accelerated computing deployed in new patterns. We'll cover how telecommunications companies can tackle ...Read More
Abstract:
We'll examine what the evolution of the 5G network means for telecommunications providers and examine how supporting the jump to 5G will require accelerated computing deployed in new patterns. We'll cover how telecommunications companies can tackle the data tsunami that will emerge with 5G, explore the new intelligent edge, and share solutions to the challenges of 5G. We will also provide examples of application deployments to the edge and their use cases.  Back
 
Topics:
5G & Edge
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9756
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Abstract:
We'll talk about applying ML/AI to the crucial task of identifying and isolating faults in computer and telecommunications networks. A problem with part or all of a single device can quickly propagate through the network, making it essential to iden ...Read More
Abstract:
We'll talk about applying ML/AI to the crucial task of identifying and isolating faults in computer and telecommunications networks. A problem with part or all of a single device can quickly propagate through the network, making it essential to identify a fault before it causes a hardware component to fail. We'll discuss cost-effective expert systems for network monitoring that are designed to minimize the number of service-affecting incidents, while keeping development, personnel, and maintenance costs at an acceptable level. We'll also explain how streaming telemetry enables access to real-time, model-driven, and analytics-ready data that can help with network automation, traffic optimization, and preventive troubleshooting.  Back
 
Topics:
5G & Edge, Data Center & Cloud Infrastructure
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9758
Streaming:
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Abstract:
The telecom industry is transitioning from traditional network hardware to software defined networking on commodity hardware. Many key packet processing algorithms can be made highly parallel and have already been ported to leverage GPU parallel comp ...Read More
Abstract:
The telecom industry is transitioning from traditional network hardware to software defined networking on commodity hardware. Many key packet processing algorithms can be made highly parallel and have already been ported to leverage GPU parallel computing. Numerous studies show large gains in the price/performance ratio, but simplifying assumptions about the data path, often made when evaluating parallel packet processing algorithms on GPUs, cast doubt on the achievable benefits of parallel packet processing in a real world production environment. In order to facilitate evaluations of packet processing on GPUs, many studies isolate the computational components and ignore the time intensive process of transferring data between the NIC, CPU and GPU. Our research focuses on an end-to-end solution for GPU packet processing at line speeds above 10 Gbps that includes streaming data from the NIC directly into the GPU, processing the packets, and streaming the results back out onto the network. By analyzing and optimizing the entire packet processing pipeline, the telecommunications industry can reap the enormous price/performance gains promised by using GPUs to process network packets.  Back
 
Topics:
5G & Edge
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8835
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Abstract:
We''ll show a taxi demand forecast system called "AI Taxi" that aims to maximize the driver''s benefit and minimize the passengers'' waiting time. For predicting the taxi demand in each area, we use real-time population statistics data base ...Read More
Abstract:
We''ll show a taxi demand forecast system called "AI Taxi" that aims to maximize the driver''s benefit and minimize the passengers'' waiting time. For predicting the taxi demand in each area, we use real-time population statistics data based on NTT DOCOMO''s mobile network operational data. Using spatial-temporal data, such as population, weather, and taxi-trip, we implemented a deep neural network and autoregressive prediction model called "real-time trip demand forecast system." We evaluated the system with field testing in Tokyo, using 4,440 taxi vehicles'' trip data for training and performed testing on 30 drivers. We achieved 92.9% accuracy of demand prediction and average 1,409 JPY sales improvement per driver per day.  Back
 
Topics:
5G & Edge
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8797
Streaming:
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AEC & Manufacturing
Presentation
Media
Abstract:
新型视觉计算技术正在改变建筑和城市的设计方式。无论是在设计评审和客户展示阶段,还是在设计流程的早期阶段,设计公司都在更广泛地应用照片级逼真度,以帮助改进设计决策。虚拟现实有助我们更加清晰地了解设计评审,而机器学习和深度学习让图像分析、预测和自然语言处理在工程应用中得以实现。本次讲座将回顾这些突破性技术如何促进设计团队实现创新,并将涵盖在工程领域部署和推广机器学习的趋势与挑战 ...Read More
Abstract:
新型视觉计算技术正在改变建筑和城市的设计方式。无论是在设计评审和客户展示阶段,还是在设计流程的早期阶段,设计公司都在更广泛地应用照片级逼真度,以帮助改进设计决策。虚拟现实有助我们更加清晰地了解设计评审,而机器学习和深度学习让图像分析、预测和自然语言处理在工程应用中得以实现。本次讲座将回顾这些突破性技术如何促进设计团队实现创新,并将涵盖在工程领域部署和推广机器学习的趋势与挑战。  Back
 
Topics:
AEC & Manufacturing
Type:
Talk
Event:
GTC China
Year:
2018
Session ID:
CH81002
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Abstract:
PEGATRON 的智慧家用機器人主要功能是可以利用深度學習發展的各種 AI 感知技術,例如人臉辨識、姿態偵測、火焰辨識等功能,來提供專屬家人的體驗及安全防護。其中還要介紹人臉辨識的發展過程,經過四年的發展,在 2017 我們達到 LFW 人臉辨識評估上 99.58% 的準確率。這些都是使用 NVIDIA GPU 進行訓練及部署。最後還會介紹 PEGATRON 在深度學習上發展的其他技術。 ...Read More
Abstract:
PEGATRON 的智慧家用機器人主要功能是可以利用深度學習發展的各種 AI 感知技術,例如人臉辨識、姿態偵測、火焰辨識等功能,來提供專屬家人的體驗及安全防護。其中還要介紹人臉辨識的發展過程,經過四年的發展,在 2017 我們達到 LFW 人臉辨識評估上 99.58% 的準確率。這些都是使用 NVIDIA GPU 進行訓練及部署。最後還會介紹 PEGATRON 在深度學習上發展的其他技術。  Back
 
Topics:
AEC & Manufacturing, Artificial Intelligence and Deep Learning, Intelligent Machines, IoT & Robotics
Type:
Talk
Event:
GTC Taiwan
Year:
2018
Session ID:
STW8017
Streaming:
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Abstract:
晶圓級晶粒尺寸封裝是錫凸塊將訊號直接由晶粒傳至電路板。在產品生命週期中這些錫接點會反覆受到高低溫變化造成的機械應力。錫接點的疲勞破裂便是封裝常見的失效模式。本研究使用 ANSYS Mechanical 模擬每個錫接點在五個溫度循環中的累積應變能增量,累積應變能增量較小的設計可預期會有較長壽命。此數值模型中同時有細微的晶粒特徵與相對大尺寸的電路板因此元素與節點會較一般封裝體更多,同時非線性的彈塑計算也使求解時間更為漫長。使用多核心及開啟 GPU 加速運算的效能差異將會一併比較。 ...Read More
Abstract:
晶圓級晶粒尺寸封裝是錫凸塊將訊號直接由晶粒傳至電路板。在產品生命週期中這些錫接點會反覆受到高低溫變化造成的機械應力。錫接點的疲勞破裂便是封裝常見的失效模式。本研究使用 ANSYS Mechanical 模擬每個錫接點在五個溫度循環中的累積應變能增量,累積應變能增量較小的設計可預期會有較長壽命。此數值模型中同時有細微的晶粒特徵與相對大尺寸的電路板因此元素與節點會較一般封裝體更多,同時非線性的彈塑計算也使求解時間更為漫長。使用多核心及開啟 GPU 加速運算的效能差異將會一併比較。  Back
 
Topics:
AEC & Manufacturing, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Taiwan
Year:
2018
Session ID:
STW8018
Streaming:
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Abstract:
英業達和子公司英研智能移動採用最新的深度學習物件偵測技術,包含 Faster R-CNN (FRCN) 和 Single Shot Multibox Detector (SSD) ,並針對 NVIDIA Jetson TX2 平台進行速度最佳化,故可在邊緣裝置上做到近 30 fps 即時且準確的物件偵測。我們把這個物件偵測技術運用在智慧路燈攝像頭上,不需將案場影像透過網路傳到遠端,而可在邊緣裝置上直接分析影像並自動偵測違規停車、計算人流、車流以及船隻等。 在這個演講中我們將分享在 NVIDIA Jetson TX2 平台上開發上述物件偵測技術的經驗及其在智慧路燈攝像頭上的應用。 ...Read More
Abstract:
英業達和子公司英研智能移動採用最新的深度學習物件偵測技術,包含 Faster R-CNN (FRCN) 和 Single Shot Multibox Detector (SSD) ,並針對 NVIDIA Jetson TX2 平台進行速度最佳化,故可在邊緣裝置上做到近 30 fps 即時且準確的物件偵測。我們把這個物件偵測技術運用在智慧路燈攝像頭上,不需將案場影像透過網路傳到遠端,而可在邊緣裝置上直接分析影像並自動偵測違規停車、計算人流、車流以及船隻等。 在這個演講中我們將分享在 NVIDIA Jetson TX2 平台上開發上述物件偵測技術的經驗及其在智慧路燈攝像頭上的應用。  Back
 
Topics:
AEC & Manufacturing, Intelligent Machines, IoT & Robotics, Artificial Intelligence and Deep Learning
Type:
Tutorial
Event:
GTC Taiwan
Year:
2018
Session ID:
STW8010
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Abstract:
精確的地圖資訊與可預測的交通路況資料對於智慧交通系統 (ITS)
的成敗至關重要。傳統製圖產業倚重大量的人力與時間,因此大部分的導航系統中的地圖通常會有過時的問題。另一方面,當前大部分的交通流量預測方法以統計方法建立路況預測模型,因此無法滿足許多現實世界的應用情境。這讓我們不得不重新思考如何透過深度學習的架構,搭配我們大量的地圖和歷史交通數據去加速製圖流程以及進行交通路況的預測工作。勤崴已經在 NVIDIA 的軟、硬體平台上進行開發研究,將深度學習技術運用在智慧化製圖與 AI 智慧導航上,逐步建構出未來智慧城市下的 ITS 應用情境。 ...Read More
Abstract:
精確的地圖資訊與可預測的交通路況資料對於智慧交通系統 (ITS)
的成敗至關重要。傳統製圖產業倚重大量的人力與時間,因此大部分的導航系統中的地圖通常會有過時的問題。另一方面,當前大部分的交通流量預測方法以統計方法建立路況預測模型,因此無法滿足許多現實世界的應用情境。這讓我們不得不重新思考如何透過深度學習的架構,搭配我們大量的地圖和歷史交通數據去加速製圖流程以及進行交通路況的預測工作。勤崴已經在 NVIDIA 的軟、硬體平台上進行開發研究,將深度學習技術運用在智慧化製圖與 AI 智慧導航上,逐步建構出未來智慧城市下的 ITS 應用情境。  Back
 
Topics:
AEC & Manufacturing, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Taiwan
Year:
2018
Session ID:
STW8024
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Abstract:
我們提出一個泛用且基於深度學習的 360 度視覺感知系統,用來達到顯著偵測、自我定位、以及深度預測。這些功能的完成,是基於一個新開發的通用 Padding 技術 (Cube-Padding)。此技術可以泛用在任何卷積類神經網路上,達到避免 360 度影像原始的扭曲以及圖像邊緣等問題。360 度的視覺感知系統是任何自主系統的核心技術,例如自動駕駛車、四軸飛行器、或是家用機器人對 360 度環境的感知。 ...Read More
Abstract:
我們提出一個泛用且基於深度學習的 360 度視覺感知系統,用來達到顯著偵測、自我定位、以及深度預測。這些功能的完成,是基於一個新開發的通用 Padding 技術 (Cube-Padding)。此技術可以泛用在任何卷積類神經網路上,達到避免 360 度影像原始的扭曲以及圖像邊緣等問題。360 度的視覺感知系統是任何自主系統的核心技術,例如自動駕駛車、四軸飛行器、或是家用機器人對 360 度環境的感知。  Back
 
Topics:
AEC & Manufacturing, Artificial Intelligence and Deep Learning, Intelligent Machines, IoT & Robotics
Type:
Talk
Event:
GTC Taiwan
Year:
2018
Session ID:
STW8025
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Abstract:
隨著 AI 技術的演進,眾人均期待以機器視覺輔佐智慧製造,達到高效能、精準、快速反應的智慧製造需求。 機器視覺的應用範疇將不再僅受限於量測、定位、導引、辨識等傳統應用,如何一方面加入 AI 的頭腦,即時收集、分析與處理大量資料運算,另一方面並能與其他設備協同工作,與時俱進支援邊緣運算,以及建構智慧工廠所必需的實時決策系統,乃成為新興話題與挑戰。凌華科技將與您共同探討此一方向。 ...Read More
Abstract:
隨著 AI 技術的演進,眾人均期待以機器視覺輔佐智慧製造,達到高效能、精準、快速反應的智慧製造需求。 機器視覺的應用範疇將不再僅受限於量測、定位、導引、辨識等傳統應用,如何一方面加入 AI 的頭腦,即時收集、分析與處理大量資料運算,另一方面並能與其他設備協同工作,與時俱進支援邊緣運算,以及建構智慧工廠所必需的實時決策系統,乃成為新興話題與挑戰。凌華科技將與您共同探討此一方向。  Back
 
Topics:
AEC & Manufacturing, Artificial Intelligence and Deep Learning, Intelligent Machines, IoT & Robotics
Type:
Talk
Event:
GTC Taiwan
Year:
2018
Session ID:
STW8026
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Abstract:
從任何地方存取、在大型數據集上進行協作,以及適應浮動工作人員都只是數位工作空間的挑戰。 NVIDIA 虛擬 GPU 正在破除障礙,以達成理想的數位工作空間,並支援數據中心、雲端運算和圖形密集型工作負載。 了解我們的最新版軟體如何為最嚴苛的工作流程提供 GPU 虛擬化,同時透過 GPU 即時遷移,最大限度地提高數據中心的利用率並實現最高可用性。 本議程中,與會者將能夠了解製造業、建築和工程以及其他行業的領導組織如何採用虛擬 GPU 技術。 ...Read More
Abstract:
從任何地方存取、在大型數據集上進行協作,以及適應浮動工作人員都只是數位工作空間的挑戰。 NVIDIA 虛擬 GPU 正在破除障礙,以達成理想的數位工作空間,並支援數據中心、雲端運算和圖形密集型工作負載。 了解我們的最新版軟體如何為最嚴苛的工作流程提供 GPU 虛擬化,同時透過 GPU 即時遷移,最大限度地提高數據中心的利用率並實現最高可用性。 本議程中,與會者將能夠了解製造業、建築和工程以及其他行業的領導組織如何採用虛擬 GPU 技術。  Back
 
Topics:
AEC & Manufacturing
Type:
Talk
Event:
GTC Taiwan
Year:
2018
Session ID:
STW8032
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Abstract:
Learn the benefits that virtualization provides for an architecture and engineering design firm, along with the journey through the advancements in virtualization technology it took to finally meet the graphics-intensive needs of our design soft ...Read More
Abstract:

Learn the benefits that virtualization provides for an architecture and engineering design firm, along with the journey through the advancements in virtualization technology it took to finally meet the graphics-intensive needs of our design software. We'll share our experiences in how virtualization allows a large company, with over 15 offices and 1,000 people worldwide, to collaborate and work as a single firm. We'll show some cost comparisons with virtualization, along with their management benefits and requirements. We'll also look at the methods we used to set and test metrics specific to our requirements, and follow the results of those metrics through the changes in graphics virtualization technology.

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Topics:
AEC & Manufacturing, GPU Virtualization, Data Center & Cloud Infrastructure
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7174
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Abstract:
We'll discuss Bunsen, a large-scale visualization framework that prepares and optimizes engineering, architectural, and other CAD and CAM data. Bunsen is a cloud-hosted solution that reads and writes various industry standard file formats (f ...Read More
Abstract:

We'll discuss Bunsen, a large-scale visualization framework that prepares and optimizes engineering, architectural, and other CAD and CAM data. Bunsen is a cloud-hosted solution that reads and writes various industry standard file formats (for example, Revit, SOLIDWORKS, Rhino, Maya, Max, Siemens, and Microstation) and provides powerful tools for processing and conversion. It runs on public cloud solutions, such as AWS or Google, or within your own data center or on-prem cloud. All hardware and software are provisioned in the cloud and are usable from any laptop, tablet, or phone with a web browser. Within Bunsen, the user can create sets of reusable rules to process data for visualization and output. You can think of these rules as company standards relating to lighting, materials, colors, and how to reduce object complexity. Possible visualization output platforms include rendering and animation, virtual reality, augmented reality, and real-time game engines, such as Unreal and Unity. Bunsen doesn't mean you change your workflow -- it is a framework to automate, document, and accelerate your existing workflows.

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Topics:
AEC & Manufacturing, AEC & Manufacturing, Rendering & Ray Tracing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7474
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Abstract:
We'll present, in a case study driven presentation, specific examples of how GPU-enabled deep neural networks are powering new methods for analyzing the content of photos and videos from industrial contexts. First, we'll present a collab ...Read More
Abstract:

We'll present, in a case study driven presentation, specific examples of how GPU-enabled deep neural networks are powering new methods for analyzing the content of photos and videos from industrial contexts. First, we'll present a collaboration between Smartvid.io and Engineering News-Record, the leading publication in the architecture, engineering, and construction vertical. This ongoing initiative leverages computer vision techniques and semantic approaches to help identify and indicate safe and unsafe situations in jobsite photos. Second, we'll present a collaboration with Arup, a London-based engineering firm, on the use of specific classifiers to localize and measure cracks and related defects in infrastructure.

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Topics:
AEC & Manufacturing, Artificial Intelligence and Deep Learning, AI Startup
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7575
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Abstract:
Learn how Gensler is using the latest technology in virtual reality across all aspects of the design process for the AEC industry. We'll cover how VR has added value to the process when using different kinds of VR solutions. Plus we'll t ...Read More
Abstract:

Learn how Gensler is using the latest technology in virtual reality across all aspects of the design process for the AEC industry. We'll cover how VR has added value to the process when using different kinds of VR solutions. Plus we'll talk about some of the challenges Gensler has faced with VR in terms of hardware, software, and workflows. Along with all of this, NVIDIA's latest VR visualization tools are helping with the overall process and realism of our designs.

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Topics:
AEC & Manufacturing, Virtual Reality & Augmented Reality
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7614
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Abstract:
Learn about the unique challenges being solved using deep learning on GPUs in a large-scale mass customization of medical devices. Deep neural networks have been successfully applied to some of the most difficult problems in computer vision, nat ...Read More
Abstract:

Learn about the unique challenges being solved using deep learning on GPUs in a large-scale mass customization of medical devices. Deep neural networks have been successfully applied to some of the most difficult problems in computer vision, natural language processing, and robotics. But we still haven't seen the full potential of this technology used in manufacturing. Glidewell Labs daily produces thousands of patient specific items, such as dental restorations, implants, and appliances. Our goal is to make high-quality restorative dentistry affordable to more patients. This goal can only be achieved with flexible, highly autonomous CAD/CAM systems, which rely on AI for real-time decision making.

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Topics:
AEC & Manufacturing, Artificial Intelligence and Deep Learning, Healthcare and Life Sciences
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7114
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Abstract:
Learn about a new solid modeling approach created to provide support for customer- and patient-specific design and additive manufacturing (3D printing) with graded materials and properties. The new modeling approach involves a hybrid of function-base ...Read More
Abstract:
Learn about a new solid modeling approach created to provide support for customer- and patient-specific design and additive manufacturing (3D printing) with graded materials and properties. The new modeling approach involves a hybrid of function-based (implicit) modeling and voxel modeling; models consist of function values on a regular grid (along with a simple interpolant), so meshing/triangulation of objects' surfaces and/or volumes is avoided. Learn the basic ideas behind the modeling approach and see demonstrations of: (1) CUDA-accelerated, real-time interactions between digital models imported from CAD systems and digitized/scanned models, (2) design and fabrication of objects with graded materials/properties, and (3) initial results of CUDA-accelerated methods for mesh-free property evaluation and analysis.  Back
 
Topics:
AEC & Manufacturing, Healthcare and Life Sciences
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7131
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Abstract:
Introducing the transition from traditional workstation to immersive experience workspace, hear about novel NVIDIA and ESI technologies to combine streaming and virtualization for GPUs to provide scalable immersive virtual and augmented reality. We' ...Read More
Abstract:
Introducing the transition from traditional workstation to immersive experience workspace, hear about novel NVIDIA and ESI technologies to combine streaming and virtualization for GPUs to provide scalable immersive virtual and augmented reality. We'll discuss the challenges in advancing to the immersive workspace for mobile, desk-side, or team-size immersive experiences through on-premise and cloud-based virtual engineering applications.  Back
 
Topics:
AEC & Manufacturing, GPU Virtualization, Virtual Reality & Augmented Reality
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7203
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Abstract:
Honda's evolutionary new project?internally called the "Next-gen Engineering Workstation (EWS) Project"?is designed to optimize usage of our CAD-VDI environment for R&D offices and factories. The project's challenges are to ...Read More
Abstract:

Honda's evolutionary new project?internally called the "Next-gen Engineering Workstation (EWS) Project"?is designed to optimize usage of our CAD-VDI environment for R&D offices and factories. The project's challenges are to move from the existing physical EWS and pass-through VDI environments to an NVIDIA GRID vGPU environment. All while improving user density (CCU/server), usage monitoring, resource optimization for designers, and flexible resource reallocation. Honda successfully deployed more than 4,000 concurrent CAD-VDI users in its initial phase, with aggressive plans to further increase utilization. This session will review the project's challenges and Honda's future vision.

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Topics:
AEC & Manufacturing, GPU Virtualization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7390
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Abstract:
Improvements in 3D printing allow for unique processes, finer details, better quality control, and a wider range of materials as printing hardware improves. With these improvements comes the need for greater computational power and control over ...Read More
Abstract:

Improvements in 3D printing allow for unique processes, finer details, better quality control, and a wider range of materials as printing hardware improves. With these improvements comes the need for greater computational power and control over 3D-printed objects. We introduce NVIDIA GVDB Voxels as an open source SDK for voxel-based 3D printing workflows. Traditional workflows are based on processing polygonal models and STL files for 3D printing. However, such models don't allow for continuous interior changes in color or density, for descriptions of heterogeneous materials, or for user-specified support lattices. Using the new NVIDIA GVDB Voxels SDK, we demonstrate practical examples of design workflows for complex 3D printed parts with high-quality ray-traced visualizations, direct data manipulation, and 3D printed output.

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Topics:
AEC & Manufacturing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7425
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Abstract:
We'll present how deep learning is applied in a manufacturer's production line. Fujikura and OPTOENERGY are introducing a visual inspection system incorporating deep learning in the production process of semiconductor lasers. The same in ...Read More
Abstract:

We'll present how deep learning is applied in a manufacturer's production line. Fujikura and OPTOENERGY are introducing a visual inspection system incorporating deep learning in the production process of semiconductor lasers. The same inspection accuracy as skilled workers was achieved by optimizing the image size and the hyper parameters of a CNN model. The optimized image size is less than one quarter of the image size required for the visual inspection by skilled workers, which leads to large cost reduction of the production line. It was also confirmed that the highlighted region in the heatmaps of NG images didn't meet the criteria of the visual inspection. The visual inspection incorporating deep learning is being applied to other products such as optical fibers and electrical cables.

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Topics:
AEC & Manufacturing, Artificial Intelligence and Deep Learning, Intelligent Machines, IoT & Robotics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7623
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AI & Deep Learning Business Track (High Level)
Presentation
Media
Abstract:
Hundreds of talks and competing events crammed into a few days can be daunting. Get an overview of GTC's programs and events and how to make best use of them from Greg Estes, NVIDIA's VP of developer programs. Addressing both first-timer ...Read More
Abstract:

Hundreds of talks and competing events crammed into a few days can be daunting. Get an overview of GTC's programs and events and how to make best use of them from Greg Estes, NVIDIA's VP of developer programs. Addressing both first-timers and returning alums, Greg will cover how to get the most from your time here, including can't-miss talks and never-before-seen tech demos. He'll also cover NVIDIA's resources for developers, startups, and larger organizations, as well as training courses and networking opportunities.

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Topics:
AI & Deep Learning Business Track (High Level)
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91004
Streaming:
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Abstract:
A fireside chat with U.S. Rep. Jerry McNerney (D-Calif.), co-chair of the congressional AI caucus, and Ned Finkle, VP of Govt. Affairs, NVIDIA. Artificial Intelligence has become a front-and-center issue for policymakers. Legislative propos ...Read More
Abstract:

A fireside chat with U.S. Rep. Jerry McNerney (D-Calif.), co-chair of the congressional AI caucus, and Ned Finkle, VP of Govt. Affairs, NVIDIA. Artificial Intelligence has become a front-and-center issue for policymakers. Legislative proposals to encourage AI development and head off possible harms are gaining traction, and the Administration is working to build a national strategy. This fireside chat will give enterprises and researchers a first-hand look at how key Members of Congress are approaching AI, as well as what policies they're advocating for and expect.

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Topics:
AI & Deep Learning Business Track (High Level)
Type:
Panel
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91006
Streaming:
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Abstract:
Innovation is critical to every enterprise, but rarely convenient. We'll explore how business leaders approach the dual responsibility of building business growth and efficiencies through AI innovation while navigating the change cycles that every e ...Read More
Abstract:
Innovation is critical to every enterprise, but rarely convenient. We'll explore how business leaders approach the dual responsibility of building business growth and efficiencies through AI innovation while navigating the change cycles that every enterprise experiences.  Back
 
Topics:
AI & Deep Learning Business Track (High Level)
Type:
Panel
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91015
Streaming:
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Abstract:
As machine learning and deep learning techniques evolve into mainstream adoption, the architectural considerations for platforms that support large-scale production deployments of AI applications change significantly. How do you ensure IO bottlenecks ...Read More
Abstract:
As machine learning and deep learning techniques evolve into mainstream adoption, the architectural considerations for platforms that support large-scale production deployments of AI applications change significantly. How do you ensure IO bottlenecks are eliminated to keep your GPU-Powered AI rocket ship fueled with data? How do you address the issues of data gravity, data scaling, and data economics to support Petabyte-sized data sets? How do you simplify data management and minimize business risk and life cycle costs of large scale AI platforms? Well address these questions, discuss key business and architectural requirements for compute and storage, and explain how enterprises can achieve the maximum benefit from AI platforms that align with these requirements. Well also introduce the Dell, EMC, and NVIDIA solution portfolio which makes AI simple, flexible, and accessible.  Back
 
Topics:
AI & Deep Learning Business Track (High Level), AI & Deep Learning Research
Type:
Sponsored Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91016
Streaming:
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Abstract:
This customer panel brings together AI implementers who have deployed deep learning at scale. The discussion will focus on specific technical challenges they faced, solution design considerations, and best practices learned from implementing the ...Read More
Abstract:

This customer panel brings together AI implementers who have deployed deep learning at scale. The discussion will focus on specific technical challenges they faced, solution design considerations, and best practices learned from implementing their respective solutions.

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Topics:
AI & Deep Learning Business Track (High Level), Data Center & Cloud Infrastructure, Deep Learning & AI Frameworks
Type:
Panel
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9121
Streaming:
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Abstract:
The size and complexity of problems that can be tackled with machine learning, and particularly deep learning, represents a new approach to business problem-solving. Learn to identify use cases for ML and acquire best practices to frame problems in a ...Read More
Abstract:
The size and complexity of problems that can be tackled with machine learning, and particularly deep learning, represents a new approach to business problem-solving. Learn to identify use cases for ML and acquire best practices to frame problems in a way that key stakeholders and senior management can understand and support. In our talk, we'll help delegates set the stage for delivering successful ML-based solutions to their businesses.  Back
 
Topics:
AI & Deep Learning Business Track (High Level), Accelerated Data Science
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9295
Streaming:
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Abstract:
Designed for the business leader, this session is a getting started primer for deep learning in the enterprise. Through cross-industry use cases, panelists will discuss adoption considerations, developing teams, building proof-of-concepts, and measur ...Read More
Abstract:
Designed for the business leader, this session is a getting started primer for deep learning in the enterprise. Through cross-industry use cases, panelists will discuss adoption considerations, developing teams, building proof-of-concepts, and measurement.  Back
 
Topics:
AI & Deep Learning Business Track (High Level)
Type:
Panel
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9937
Streaming:
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Abstract:
Artificial Intelligence has the potential to profoundly affect our world and lives. In this era of constant change, how do organizations keep up? We'll discuss the forces that drive technology forward and the technology trends, including AI, ...Read More
Abstract:

Artificial Intelligence has the potential to profoundly affect our world and lives. In this era of constant change, how do organizations keep up? We'll discuss the forces that drive technology forward and the technology trends, including AI, that can help organizations remain relevant in a world of constant transformation.

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Topics:
AI & Deep Learning Business Track (High Level)
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9938
Streaming:
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Abstract:
By 2035, artificial intelligence could increase productivity by 40 percent or more. Manufacturing, healthcare, retail, and other key industries will benefit. We'll discuss how we're driving operational efficiencies within our organizations with AI ...Read More
Abstract:
By 2035, artificial intelligence could increase productivity by 40 percent or more. Manufacturing, healthcare, retail, and other key industries will benefit. We'll discuss how we're driving operational efficiencies within our organizations with AI applications, from getting started to advanced systems.  Back
 
Topics:
AI & Deep Learning Business Track (High Level)
Type:
Panel
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9941
Streaming:
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Abstract:
Retailers are on the front lines of using AI as an instrument of innovation, ranging from in-store experiences to back-end efficiencies. We'll provide practical insights from retail industry leaders that outline success criteria that any industry ca ...Read More
Abstract:
Retailers are on the front lines of using AI as an instrument of innovation, ranging from in-store experiences to back-end efficiencies. We'll provide practical insights from retail industry leaders that outline success criteria that any industry can apply to its strategy, from optimization of supply chain routes to spend management.  Back
 
Topics:
AI & Deep Learning Business Track (High Level)
Type:
Panel
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9942
Streaming:
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Abstract:
Data quality is a challenge all businesses face as they move forward with AI applications. Real-world data can be flawed, and attempting to create it for training and inference is sometimes too dangerous or costly to tackle. We'll discuss synthetic ...Read More
Abstract:
Data quality is a challenge all businesses face as they move forward with AI applications. Real-world data can be flawed, and attempting to create it for training and inference is sometimes too dangerous or costly to tackle. We'll discuss synthetic data, which can be created in controlled environments, even virtual ones. We'll explain why we see it as the future of data generation that will help businesses succeed.  Back
 
Topics:
AI & Deep Learning Business Track (High Level)
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9943
Streaming:
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Abstract:
How has AI had an effect on the world around us? Hear the extraordinary stories showcased in the I am AI keynote video and documentary series, and learn how humans and technology are working together to solve the grand challenges of our time. ...Read More
Abstract:
How has AI had an effect on the world around us? Hear the extraordinary stories showcased in the I am AI keynote video and documentary series, and learn how humans and technology are working together to solve the grand challenges of our time.  Back
 
Topics:
AI & Deep Learning Business Track (High Level)
Type:
Panel
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9944
Streaming:
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Abstract:
Healthcare has been on the frontlines of AI adoption for some time, using it to solve critical problems impacting humanity at large. What can other industries learn from healthcare AI innovation, in terms of applications and benchmarks? What insights ...Read More
Abstract:
Healthcare has been on the frontlines of AI adoption for some time, using it to solve critical problems impacting humanity at large. What can other industries learn from healthcare AI innovation, in terms of applications and benchmarks? What insights can be shared about cross-industry concerns such as privacy and security? How will healthcare further evolve now that data-centric models are driving new models?  Back
 
Topics:
AI & Deep Learning Business Track (High Level)
Type:
Panel
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9989
Streaming:
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Abstract:
See how deep neural networks are trained to perform tasks with super-human accuracy and will explore which deep neural network models are best-suited for a variety of tasks.
 
Topics:
AI & Deep Learning Business Track (High Level)
Type:
Talk
Event:
GTC Europe
Year:
2018
Session ID:
E8110
Streaming:
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Abstract:
Explore the worldwide trend of implementing AI and Deep Learning solutions across a range of vertical markets as well as how they are a game-changing factor for the workflows of a company.
 
Topics:
AI & Deep Learning Business Track (High Level)
Type:
Talk
Event:
GTC Europe
Year:
2018
Session ID:
E8231
Streaming:
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Abstract:
Explore the benefits linked to investing in AI and deep learning. The journey from the decision making stage to implementation and the benefits to each business area within a company will be shown through real-life use cases.
 
Topics:
AI & Deep Learning Business Track (High Level)
Type:
Talk
Event:
GTC Europe
Year:
2018
Session ID:
E8413
Streaming:
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Abstract:
This panel will showcase how management teams can implement new AI/DL solutions quickly and effectively, by developing talented teams successfully. It will also discuss how POCs can be moved seamlessly into productive use.
 
Topics:
AI & Deep Learning Business Track (High Level)
Type:
Panel
Event:
GTC Europe
Year:
2018
Session ID:
E8417
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Speakers:
, , ,
Abstract:
This session will feature at the Women at GTC Lunch event and will explore the topic of "Leadership In The Age of AI". All attendees who support diversity and inclusion in the tech world are welcome to join.
 
Topics:
AI & Deep Learning Business Track (High Level), Artificial Intelligence and Deep Learning, Autonomous Vehicles, Virtual Reality & Augmented Reality, Genomics & Bioinformatics
Type:
Panel
Event:
GTC Europe
Year:
2018
Session ID:
E8497
Streaming:
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Speakers:
, , , , , , , ,
Abstract:
GTC Europe will feature groundbreaking work from startups using artificial intelligence to transform the world in the fields of autonomous machines, cyber security, healthcare and more. Join us to watch the hottest startups in Europe take to the ...Read More
Abstract:

GTC Europe will feature groundbreaking work from startups using artificial intelligence to transform the world in the fields of autonomous machines, cyber security, healthcare and more. Join us to watch the hottest startups in Europe take to the stage and pitch their work for a chance to win $100,000 and a DGX Station.

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Topics:
AI & Deep Learning Business Track (High Level), Artificial Intelligence and Deep Learning, Virtual Reality & Augmented Reality, Autonomous Vehicles, Intelligent Machines, IoT & Robotics
Type:
Panel
Event:
GTC Europe
Year:
2018
Session ID:
E8499
Streaming:
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Abstract:
Innovation can take many forms, and led by varying stakeholders across an organization. One successful model is utilizing AI for Social Good to drive a proof-of-concept that will advance a critical strategic goal. The Data Science Bowl (DSB) is ...Read More
Abstract:

Innovation can take many forms, and led by varying stakeholders across an organization. One successful model is utilizing AI for Social Good to drive a proof-of-concept that will advance a critical strategic goal. The Data Science Bowl (DSB) is an ideal example, launched by Booz Allen Hamilton in 2014, it galvanizes thousands of data scientists to participate in competitions that will have have far reaching impact across key industries such as healthcare. This session will explore the DSB model, as well as look at other ways organizations are utilizing AI for Social Good to create business and industry transformation.

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Topics:
AI & Deep Learning Business Track (High Level)
Type:
Panel
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8953
Streaming:
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Abstract:
From healthcare to financial services to retail, businesses are seeing unprecedented levels of efficiencies and productivity, which will only continue to rise and transform how companies operate. This session will look at how Accenture as an ent ...Read More
Abstract:

From healthcare to financial services to retail, businesses are seeing unprecedented levels of efficiencies and productivity, which will only continue to rise and transform how companies operate. This session will look at how Accenture as an enterprise is optimizing itself in the age of AI, as well as how it guides its customers to success. A look at best practices, insights, and measurement to help the audience inform their AI roadmap and journey.

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Topics:
AI & Deep Learning Business Track (High Level)
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8984
Streaming:
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Abstract:
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 ...Read More
Abstract:

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.

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Topics:
AI & Deep Learning Business Track (High Level), 5G & Edge, Speech & Language Processing, AI Startup
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8274
Streaming:
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Business Track (High Level), AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8939
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Business Track (High Level)
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8954
Streaming:
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Abstract:
We are still in the early stages of AI, and its impact on industries is already significant - from healthcare to financial services to retail. Businesses are seeing unprecedented levels of efficiencies and productivity, which will only continue to ri ...Read More
Abstract:
We are still in the early stages of AI, and its impact on industries is already significant - from healthcare to financial services to retail. Businesses are seeing unprecedented levels of efficiencies and productivity, which will only continue to rise and transform how companies operate. This session will explore the progress of AI adoption over the last year, the industries that are leaping ahead, new AI innovations that will serve cross-industry concerns, and what businesses should expect in terms of adoption maturity in 2018.  Back
 
Topics:
AI & Deep Learning Business Track (High Level)
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8952
Streaming:
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Abstract:
Has your team developed an AI proof-of-concept with promising metrics? Next step is to broaden the scope to impact larger areas of the enterprise. With its unique challenges and complexities, scaling POCs across multiple business units is a sign ...Read More
Abstract:

Has your team developed an AI proof-of-concept with promising metrics? Next step is to broaden the scope to impact larger areas of the enterprise. With its unique challenges and complexities, scaling POCs across multiple business units is a significant part of any company''s AI roadmap. This session will look at best practices, insights and success, rooted in Element AI''s experience with enterprise customers.

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Topics:
AI & Deep Learning Business Track (High Level), AI Startup
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8989
Streaming:
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Abstract:
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
Abstract:

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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Topics:
AI & Deep Learning Business Track (High Level)
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8983
Streaming:
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Abstract:
Get the latest information on how the proliferation of mobile, cloud, and IoT devices has brought us into a new era: The Extreme Data Economy. There''s a greater variety of data than ever before, and exponentially more of it, streaming i ...Read More
Abstract:

Get the latest information on how the proliferation of mobile, cloud, and IoT devices has brought us into a new era: The Extreme Data Economy. There''s a greater variety of data than ever before, and exponentially more of it, streaming in real time. Across industries, companies are turning data into an asset, above and beyond any product or service they offer. But unprecedented agility is required to keep business in motion and succeed in this post-big data era. To enable this level of agility, companies are turning to instant insight engines that are powered by thousands of advanced GPU cores, bringing unparalleled speed, streaming data analysis, visual foresight, and machine learning to break through the old bottlenecks. Learn about new data-powered use cases you''ll need to address, as well as advances in computing technology, particularly accelerated parallel computing, that will translate data into instant insight to power business in motion.

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Topics:
AI & Deep Learning Business Track (High Level), AI Startup
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8997
Streaming:
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Abstract:
In this session, you will learn how Google Cloud helps enterprises make the most out of data, and deliver customer value. We will provide an in-depth overview of the Cloud AI and Data Analytics offering that helps enterprises manage their ML lifecycl ...Read More
Abstract:
In this session, you will learn how Google Cloud helps enterprises make the most out of data, and deliver customer value. We will provide an in-depth overview of the Cloud AI and Data Analytics offering that helps enterprises manage their ML lifecycle, from data ingestion to insights and prediction. We will also demonstrate some breakthrough solutions, like AutoML, that are making ML accessible to everyone.  Back
 
Topics:
AI & Deep Learning Business Track (High Level)
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8976
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Abstract:
We'll examine business value drivers for artificial intelligence and machine learning in retail and consumer goods industries. Traditionally, traction in AI and ML has been in deep research, scientific, and technical communities. Retailers and consu ...Read More
Abstract:
We'll examine business value drivers for artificial intelligence and machine learning in retail and consumer goods industries. Traditionally, traction in AI and ML has been in deep research, scientific, and technical communities. Retailers and consumer products companies are finding great success applying AI and ML technology to distinct use cases and business challenges. Join us to hear project descriptions and customer examples where AI and ML can impact the business by increasing revenue, protecting margin, and improving consumer satisfaction.  Back
 
Topics:
AI & Deep Learning Business Track (High Level), Virtual Reality & Augmented Reality, Consumer Engagement & Personalization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8131
Streaming:
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Abstract:
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 ...Read More
Abstract:

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.

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Topics:
AI & Deep Learning Business Track (High Level), Consumer Engagement & Personalization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8265
Streaming:
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Business Track (High Level), GIS, AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S81028
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Abstract:
Spectrum Conductor with Deep Learning capabilities is an end-to-end analytics software engine for the Data Scientist, and is optimized for accelerated hardware. It's designed to support a multi-tenant, on-premises deployment for Deep Learning with a ...Read More
Abstract:
Spectrum Conductor with Deep Learning capabilities is an end-to-end analytics software engine for the Data Scientist, and is optimized for accelerated hardware. It's designed to support a multi-tenant, on-premises deployment for Deep Learning with and end-to-end solution means customers gain business value within each phase of the deep learning pipeline. In this session, we will explore the phases of the pipeline (Setup/Configuration, Data Preparation & Ingestion, Model Training, Deploy & Inference, and Model Maintenance) and provide insights into the unique IBM value for accelerating the use of Deep Learning across a wide variety of industries.  Back
 
Topics:
AI & Deep Learning Business Track (High Level)
Type:
Talk
Event:
GTC Washington D.C.
Year:
2017
Session ID:
DC7265
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Speakers:
Abstract:
GTC Fast Forward Poster program is an accelerated poster presentation program that serves as a catalyst for the advancement of an array of innovations that come from universities, research labs, and industry. The GTC Poster Review Committee sele ...Read More
Abstract:

GTC Fast Forward Poster program is an accelerated poster presentation program that serves as a catalyst for the advancement of an array of innovations that come from universities, research labs, and industry. The GTC Poster Review Committee selected the best 20 posters submitted to GTC2017. This program gives the author a chance to present his or her GPU project in front of the top technology developers working in a vast array of industries.

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Topics:
AI & Deep Learning Business Track (High Level)
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7480
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AI & Deep Learning Research
Presentation
Media
Abstract:
Learn how to design GPU-Based systems for different application scenarios. We'll explain how to design data centers for different scales, application scenarios, and standards for enterprises and hyperscalers. We'll cover AI training and inference a ...Read More
Abstract:
Learn how to design GPU-Based systems for different application scenarios. We'll explain how to design data centers for different scales, application scenarios, and standards for enterprises and hyperscalers. We'll cover AI training and inference applications and edge computing for OCP and ODCC standard data centers. We'll discuss the challenges involved and share our experience designing a GPU platform for data centers. We'll also explore problems attendees are facing and see how we can work together to solve them.  Back
 
Topics:
AI & Deep Learning Research, HPC and AI
Type:
Sponsored Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91013
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Abstract:
Learn about the latest research on improvements to text-to-speech models and workflows using Tacotron2 and Waveglow produced by NVIDIA's applied deep learning research team. In partnership with our deep learning algorithm development team, learn mor ...Read More
Abstract:
Learn about the latest research on improvements to text-to-speech models and workflows using Tacotron2 and Waveglow produced by NVIDIA's applied deep learning research team. In partnership with our deep learning algorithm development team, learn more about how Tensor Cores have made fast mixed-precision training and faster than real-time inference performance available. We'll also be showing a demo, reviewing accuracy, and performance metrics through the open source implementation available on GitHub.  Back
 
Topics:
AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91022
Streaming:
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Abstract:
In order to obtain peak performance and energy efficiency on modern deep learning architectures, such as GPUs and TPUs, it is critical to use half precision arithmetic. Compared to single precision, half precision reduces memory traffic, allowing 2x ...Read More
Abstract:
In order to obtain peak performance and energy efficiency on modern deep learning architectures, such as GPUs and TPUs, it is critical to use half precision arithmetic. Compared to single precision, half precision reduces memory traffic, allowing 2x better use of the available DRAM bandwidth. Smaller memory footprints for half precision layer activations also allow larger batch sizes and deeper network architectures to fit in the accelerator's memory during training. Finally, architectural features, such as Volta's Tensor Cores, boost the raw math throughput of half precision operations by up to 8x compared to single precision. We describe two new streamlined implementations of mixed-precision training being built into TensorFlow. The first is provided through extensions to the tf.keras API and will be available in the upcoming months. The second is based on a Grappler graph optimization pass and will work with TF 1.x graph-based models as well as future TensorFlow 2.0 models that make use of tf.function decorators. Each method is enabled using a one or two line tweak to the training script. Empirical results show that result accuracy matches that of a model trained in single-precision, while training speedup is similar to what can be achieved with hand-coded mixed precision strategies.  Back
 
Topics:
AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91029
Streaming:
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Abstract:
GPUs are powering us into the future. Through research, we will be able to see where this technology takes us next. This session serves as a catalyst for the advancement of an array of innovations that come from universities, research labs, an ...Read More
Abstract:
GPUs are powering us into the future. Through research, we will be able to see where this technology takes us next. This session serves as a catalyst for the advancement of an array of innovations that come from universities, research labs, and industry. Join us in hearing how this year’s Top 5 Poster finalists were inspired to start their research, what surprised them, and where they want to see it go next.
 
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Topics:
AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91036
Streaming:
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Abstract:
I will introduce a game developed at Johns Hopkins University/Applied Physics Laboratory called Reconnaissance Blind Chess (RBC), a chess variant where the players do not see their opponent's moves, but they can gain information about t ...Read More
Abstract:

I will introduce a game developed at Johns Hopkins University/Applied Physics Laboratory called Reconnaissance Blind Chess (RBC), a chess variant where the players do not see their opponent's moves, but they can gain information about the ground-truth board position through the use of an (imperfect) sensor.  RBC incorporates key aspects of active sensing and planning: players have to decide where to sense, use the information gained through sensing to update their board estimates, and use that world model to decide where to move.  Thus, just as chess and go have been challenge problems for decision making with complete information, RBC is intended to be a common challenge problem for decision making under uncertainty.  After motivating the game concept and its relationship to other chess variants, I will describe the current rules of RBC as well as other potential rulesets, give a short introduction to the game implementation and bot API, and discuss some of our initial research on the complexity of RBC as well as bot algorithm

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Topics:
AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91044
Streaming:
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Abstract:
We'll discuss how using neural networks and deep reinforcement learning can be used to design potential drug candidates. The pharmaceutical industry is crying out for a revolution in thinking and practice; the traditional methods of drug discovery a ...Read More
Abstract:
We'll discuss how using neural networks and deep reinforcement learning can be used to design potential drug candidates. The pharmaceutical industry is crying out for a revolution in thinking and practice; the traditional methods of drug discovery and development are no longer working well. To continue to prosper, either R&D costs must be lowered or the rate of discovery for the new drugs must be drastically increased. We'll talk about how AI offers an opportunity to transform the field and dramatically accelerate the design of new drug candidates. The unique proposition of AI is the ability to learn directly from past experience and capture hidden dependences from both structured and unstructured data. As the chemical data is getting bigger, deep learning methods coupled with fast GPU computations make it possible to process vast amounts of information to find clinically relevant relationships and overcome drug discovery bottlenecks.  Back
 
Topics:
AI & Deep Learning Research, Computational Biology & Chemistry, AI in Healthcare
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9110
Streaming:
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Abstract:
We'll introduce Data2Vis, a neural translation model for automatically generating visualizations from given datasets. We formulate visualization generation as a sequence-to-sequence translation problem in which data is mapped to visualization specif ...Read More
Abstract:
We'll introduce Data2Vis, a neural translation model for automatically generating visualizations from given datasets. We formulate visualization generation as a sequence-to-sequence translation problem in which data is mapped to visualization specifications in a declarative language. We'll discuss how we train a multilayered attention-based encoder-decoder model on a corpus of visualization specifications. Qualitative results show that the model learns the vocabulary and syntax for valid visualization specifications, appropriate transformations, and how to use common data-selection patterns occurring within data visualizations. We'll describe how Data2Vis generates high-quality visualizations comparable to manual efforts in a fraction of the time, and how it has the potential to learn more complex visualization strategies at scale. We will also provide guidance on training such a model using the Cloudera Datascience Workbench and explore uses for Data2Vis within visualization tools.  Back
 
Topics:
AI & Deep Learning Research, AI Application, Deployment & Inference, Programming Languages
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9140
Streaming:
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Abstract:
We'll discuss an implementation of GPU convolution that favors coalesced accesses without requiring prior data transformations. Convolutions are the core operation of deep learning applications based on convolutional neural networks. Current ...Read More
Abstract:

We'll discuss an implementation of GPU convolution that favors coalesced accesses without requiring prior data transformations. Convolutions are the core operation of deep learning applications based on convolutional neural networks. Current GPU architectures are typically used for training deep CNNs, but some state-of-the-art implementations are inefficient for some commonly used network configurations. We'll discuss experiments that used our new implementation, which yielded notable performance improvements including up to 2.29X speedups in a wide range of common CNN configurations.

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Topics:
AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9218
Streaming:
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Abstract:
Learning long-term dependencies in extended temporal sequences requires credit assignment to events far in the past. The most common method for training recurrent neural networks, backpropagation through time, requires credit information to be propag ...Read More
Abstract:
Learning long-term dependencies in extended temporal sequences requires credit assignment to events far in the past. The most common method for training recurrent neural networks, backpropagation through time, requires credit information to be propagated backwards through every single step of the forward computation, potentially over thousands or millions of time steps. We'll describe how this becomes computationally expensive or even infeasible when used with long sequences. Although biological brains are unlikely to perform such detailed reverse replay over very long sequences of internal states, humans often reminded of past memories or mental states associated with their current mental states. We'll discuss the hypothesis that such memory associations between past and present could be used for credit assignment through arbitrarily long sequences, propagating the credit assigned to the current state to the associated past state.  Back
 
Topics:
AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9251
Streaming:
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Abstract:
Learn how to apply deep learning technologies for building robust and scalable dialogue systems with deeper understanding of the classic pipelines and final out more about the benchmark of models of prior work. We'll give an overview of dialogue res ...Read More
Abstract:
Learn how to apply deep learning technologies for building robust and scalable dialogue systems with deeper understanding of the classic pipelines and final out more about the benchmark of models of prior work. We'll give an overview of dialogue research and details state-of-the-art end-to-end neural dialogue systems for both task-oriented and social chit-chat conversations.  Back
 
Topics:
AI & Deep Learning Research, Speech & Language Processing
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9276
Streaming:
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Abstract:
We'll discuss Alibaba's PAI tensor accelerator and optimizer (PAI-Tao), an elaborately implemented and optimized AI engine for deep learning training and inference tasks. PAI-Tao is designed with a data-driven and compiler-oriented approach. It per ...Read More
Abstract:
We'll discuss Alibaba's PAI tensor accelerator and optimizer (PAI-Tao), an elaborately implemented and optimized AI engine for deep learning training and inference tasks. PAI-Tao is designed with a data-driven and compiler-oriented approach. It periodically collects online running statistics to provide insights for optimization and uses collected statistics to help drive the real optimization work. We'll outline how PAI-Tao's compiler-oriented design can better accommodate diversified and fast-changing AI workloads.  Back
 
Topics:
AI & Deep Learning Research, Accelerated Data Science
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9280
Streaming:
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Abstract:
Training and tuning models with lengthy training cycles like those in deep learning can be extremely expensive and may sometimes involve techniques that degrade performance. We'll explore recent research on optimization strategies to efficiently tun ...Read More
Abstract:
Training and tuning models with lengthy training cycles like those in deep learning can be extremely expensive and may sometimes involve techniques that degrade performance. We'll explore recent research on optimization strategies to efficiently tune these types of deep learning models. We will provide benchmarks and comparisons to other popular methods for optimizing the models, and we'll recommend valuable areas for further applied research.  Back
 
Topics:
AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9313
Streaming:
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Abstract:
We will discuss a deep learning-based method for improving the quality of 3D reconstruction performed by time-of-flight cameras. Scene motion, multiple reflections, and sensor noise introduce artifacts in the depth reconstruction performed by these s ...Read More
Abstract:
We will discuss a deep learning-based method for improving the quality of 3D reconstruction performed by time-of-flight cameras. Scene motion, multiple reflections, and sensor noise introduce artifacts in the depth reconstruction performed by these sensors. We'll explain our proposed two-stage, deep-learning approach to address all of these sources of artifacts simultaneously. We'll also introduce FLAT, a synthetic dataset of 2000 ToF measurements that capture all of these nonidealities and can be used to simulate different hardware. Using the Kinect camera as a baseline, we show improved reconstruction errors on simulated and real data, as compared with state-of-the-art methods.  Back
 
Topics:
AI & Deep Learning Research, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9318
Streaming:
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Abstract:
In recent year, there is growing interest in advancing AI in the reasoning field, as reasoning is one of the main abilities associated with intelligence. Deep learning performs exceptionally well at pattern recognition and in recent times, it is adva ...Read More
Abstract:
In recent year, there is growing interest in advancing AI in the reasoning field, as reasoning is one of the main abilities associated with intelligence. Deep learning performs exceptionally well at pattern recognition and in recent times, it is advancing into the reasoning field, from relational networks for question answering and transparency-by-design networks for visual reasoning. In this talk, we will share an alternate and complementary paradigm for performing reasoning with a type theoretic approach.  Back
 
Topics:
AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9383
Streaming:
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Abstract:
Learn how to achieve real-world speedup of neural networks using structural sparsity. Structural sparsity reduces the number of weights and computations in a way that's suitable for hardware acceleration. Over-parameterized neural networks waste mem ...Read More
Abstract:
Learn how to achieve real-world speedup of neural networks using structural sparsity. Structural sparsity reduces the number of weights and computations in a way that's suitable for hardware acceleration. Over-parameterized neural networks waste memory and energy. Techniques like pruning or factorization can alleviate this during inference but they often increase training time, and achieving real-world speedups remains difficult. We'll explain how biology-inspired techniques can reduce the number of weights from quadratic to linear in the number of neurons. Compared to fully connected neural networks, these structurally sparse neural networks achieve large speedups during both training and inference, while maintaining or even improving model accuracy. We'll discuss hardware considerations and results for feed-forward and recurrent networks.  Back
 
Topics:
AI & Deep Learning Research, Algorithms & Numerical Techniques
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9389
Streaming:
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Abstract:
MATLAB's deep learning, visualization, and C++/CUDA code generation technology make it a uniquely complete solution for your entire AI workflow. In MATLAB, you can easily manage data, perform complex image and signal processing, prototype and train ...Read More
Abstract:
MATLAB's deep learning, visualization, and C++/CUDA code generation technology make it a uniquely complete solution for your entire AI workflow. In MATLAB, you can easily manage data, perform complex image and signal processing, prototype and train deep networks, and deploy to your desktop, embedded or cloud environments. Using GPU Coder technology MATLAB generates CUDA kernels that optimize loops and memory access, and C++ that leverages cuDNN and TensorRT, providing the fastest deep network inference of any framework. With MATLAB's NVIDIA docker container available through the NVIDIA GPU Cloud, you can now easily access all this AI power, deploy it in your cloud or DGX environment, and get up and running in seconds. In this presentation we will demonstrate a complete end-to-end workflow that starts from 'docker run', prototypes and trains a network on a multi-GPU machine in the cloud, and ends with a highly optimized inference engine to deploy to data centers, clouds, and embedded devices.  Back
 
Topics:
AI & Deep Learning Research, Data Center & Cloud Infrastructure
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9469
Streaming:
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Abstract:
New to ML? Want to get started using TensorFlow together with GPUs? We will cover how you should use TensorFlow APIs to define and train your models, and discuss best practices for distributing the training workloads to multiple GPUs. We will also lo ...Read More
Abstract:
New to ML? Want to get started using TensorFlow together with GPUs? We will cover how you should use TensorFlow APIs to define and train your models, and discuss best practices for distributing the training workloads to multiple GPUs. We will also look at why GPUs are so great for machine learning workloads. This talk is appropriate for beginners who want to learn what TensorFlow can do.  Back
 
Topics:
AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9517
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Abstract:
Learn about object detection and how GPUs are boosting the field in the PyTorch framework. As part of our talk, we'll discuss GPU implementation, including the efficiency-accuracy tradeoff and cluster deployment. We'll also delve into the latest ob ...Read More
Abstract:
Learn about object detection and how GPUs are boosting the field in the PyTorch framework. As part of our talk, we'll discuss GPU implementation, including the efficiency-accuracy tradeoff and cluster deployment. We'll also delve into the latest object-detection research.  Back
 
Topics:
AI & Deep Learning Research, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9551
Streaming:
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Abstract:
Learn how to quickly build robust deep neural networks for visual-recognition tasks using information generated directly from the human brain. We will present a novel active learning framework that combines fast image presentation, real-time brainwav ...Read More
Abstract:
Learn how to quickly build robust deep neural networks for visual-recognition tasks using information generated directly from the human brain. We will present a novel active learning framework that combines fast image presentation, real-time brainwave classification, and the use of classification score for optimizing the loss function. We'll share examples of using the proposed framework on GPUs to train neural networks and show that our solution provides faster convergence and higher performance than traditional methods.  Back
 
Topics:
AI & Deep Learning Research, Accelerated Data Science
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9554
Streaming:
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Abstract:
We'll introduce a method for constructing an accurate prediction model from limited data in machine learning, one of the most important tasks in machine learning. We'll discuss unsupervised domain adaptation for open set data and a visual question- ...Read More
Abstract:
We'll introduce a method for constructing an accurate prediction model from limited data in machine learning, one of the most important tasks in machine learning. We'll discuss unsupervised domain adaptation for open set data and a visual question-generation method to acquire knowledge of unknown object categories.  Back
 
Topics:
AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9598
Streaming:
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Abstract:
We'll describe joint NVIDIA-Amazon work to build AI assistive tools for the caretakers of the Amazon Biospheres. We train deep autoencoders on time-series sensor streams for monitoring anomalies in the micro-climate. Our talk will cover how we deplo ...Read More
Abstract:
We'll describe joint NVIDIA-Amazon work to build AI assistive tools for the caretakers of the Amazon Biospheres. We train deep autoencoders on time-series sensor streams for monitoring anomalies in the micro-climate. Our talk will cover how we deploy convolutional architectures for tracking plant stress levels using time-lapse vision models. We'll outline how we try to use best practices for edge-to-cloud AI and how we built a workflow to train models on EC2.P3 instances (NVIDIA Tesla V100 GPUs on AWS SageMaker). We'll also discuss how we optimize models for inference using TensorRT and subsequently deploy those models on NVIDIA Jetson TX2s for the biosphere.  Back
 
Topics:
AI & Deep Learning Research, Intelligent Machines, IoT & Robotics, AI Application, Deployment & Inference
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9627
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Abstract:
We'll introduce several deep angular/hyperpherical learning frameworks and their applications in computer vision. Deep angular/hyperspherical learning provides state-of-the-art performance for general image classification and face recognition proble ...Read More
Abstract:
We'll introduce several deep angular/hyperpherical learning frameworks and their applications in computer vision. Deep angular/hyperspherical learning provides state-of-the-art performance for general image classification and face recognition problems. We'll talk about the motivation behind this type of learning and introduce some relevant variants under this framework. Deep hyperspherical learning has diverse applications in computer vision, and can also be used for learning neural network architectures and improving neural network generalization. We'll also discuss a few open problems in this framework and talk about some potential applications.  Back
 
Topics:
AI & Deep Learning Research, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9631
Streaming:
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Abstract:
Learn why deep learning scales so well and how to apply it to important open problems. Deep learning has enabled rapid progress in diverse problems in vision, speech, and beyond. Driving this progress are breakthroughs in algorithms that can harness ...Read More
Abstract:
Learn why deep learning scales so well and how to apply it to important open problems. Deep learning has enabled rapid progress in diverse problems in vision, speech, and beyond. Driving this progress are breakthroughs in algorithms that can harness massive datasets and powerful compute accelerators like GPUs. We'll combine theoretical and experiment insights to help explain why deep learning scales predictably with bigger datasets and faster computers. We'll also show how some problems are relatively easier than others and how to tell the difference. Learn about examples of open problems that cannot be solved by individual computers, but are within reach of the largest machines in the world. We'll also make the case for optimizing data centers to run AI workloads. Finally, we'll outline a high-level architecture for an AI datacenter, and leave you with powerful tools to reach beyond human accuracy to confront some of the hardest open problems in computing.  Back
 
Topics:
AI & Deep Learning Research, HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9643
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Abstract:
Academic design of deep neural networks has historically focused on maximizing accuracy at any cost. However, many practical applications have real-world constraints such as model size, computational complexity (FLOPs), or inference latency, as well ...Read More
Abstract:
Academic design of deep neural networks has historically focused on maximizing accuracy at any cost. However, many practical applications have real-world constraints such as model size, computational complexity (FLOPs), or inference latency, as well as physical hardware performance, that need to be considered. We'll discuss our MorphNet solution, an approach to automate the design of neural nets with constraint-specific and hardware-specific tradeoffs while being lightweight and scalable to large data sets. We show how MorphNet can be used to design neural nets that reduce model size, FLOP count, or inference latency with the same accuracy across different domains such as ImageNet, OCR, and AudioSet. Finally, we show how MorphNet designs different architectures when optimizing for P100 and V100 platforms.  Back
 
Topics:
AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9645
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Abstract:
In this talk, I'll discuss several semi-supervised learning applications from our recent work in applied deep learning research at NVIDIA. I'll first discuss video translation, which renders new scenes using models learned from real-world videos. W ...Read More
Abstract:
In this talk, I'll discuss several semi-supervised learning applications from our recent work in applied deep learning research at NVIDIA. I'll first discuss video translation, which renders new scenes using models learned from real-world videos. We take real world videos, analyze them using existing computer vision techniques such as pose estimation or semantic segmentation, and then train generative models to invert these poses or segmentations back to videos. In deployment, we then render novel sketches using these models. I'll then discuss work on large-scale language modeling, where a model trained to predict text, piece by piece, on a large dataset is then finetuned with small amounts of labeled data to solve problems like emotion classification. Finally, I'll discuss WaveGlow, our flow-based generative model for the vocoder stage of speech synthesis, that combines a simple log-likelihood based training procedure with very fast and efficient inference. Because semi-supervised learning allows us to try tackling problems where large amounts of labels would be prohibitively expensive to create, it opens the scope of problems to which we can apply machine learning.  Back
 
Topics:
AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9686
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Abstract:
We'll talk about how we're incorporating physics into deep learning algorithms. Standard deep learning algorithms are based on a function-fitting approach that does not exploit any domain knowledge or constraints. This makes them unsuitable for app ...Read More
Abstract:
We'll talk about how we're incorporating physics into deep learning algorithms. Standard deep learning algorithms are based on a function-fitting approach that does not exploit any domain knowledge or constraints. This makes them unsuitable for applications like robotics that require safety or stability guarantees. These algorithms also require large amounts of labeled data, which is not readily available. We'll discuss how we're overcoming these limitations by infusing physics into deep learning algorithms, and how we're applying this to stable landing of quadrotor drones. We've developed a robust deep learning-based nonlinear controller called Neural-Lander, which learns ground-effect aerodynamic forces that are hard to model. We'll also touch on how Neural-Lander can land significantly faster while maintaining stability.  Back
 
Topics:
AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9732
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Abstract:
Learn about tensors, higher-order extensions of matrices that can incorporate multiple modalities and encode higher-order relationships in data. After an introduction to tensor methods, we will discuss which tensor methods can be used in deep learnin ...Read More
Abstract:
Learn about tensors, higher-order extensions of matrices that can incorporate multiple modalities and encode higher-order relationships in data. After an introduction to tensor methods, we will discuss which tensor methods can be used in deep learning and in probabilistic modeling. We'll show how tensor contractions, which are extensions of matrix products, provide high rates of compression in a variety of neural network models. We'll also demonstrate the use of tensors for document categorization at scale through probabilistic topic models. These are available in a python library called Tensorly that provides a high-level API for tensor methods and deep tensorized architectures.  Back
 
Topics:
AI & Deep Learning Research
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9733
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Abstract:
We'll present our study on GPU optimization for deep learning with limited computational resources and share our tips and tricks for building a state-of-the-art Visual Question Answering (VQA) system. Learn about technical implementations of deep le ...Read More
Abstract:
We'll present our study on GPU optimization for deep learning with limited computational resources and share our tips and tricks for building a state-of-the-art Visual Question Answering (VQA) system. Learn about technical implementations of deep learning algorithms with GPU hardware utilization, including delayed updates and mixed-precision training, to deal with limited hardware resources while reduce training time and memory usage. We'll describe our experience designing a winning architecture for the VQA Challenge 2018 by applying deep learning tactics such as multi-level multi-modal fusion, parameter-interaction learning, and end-to-end optimization. Our techniques are all heavy computing tasks, so GPU programming plays an important role in advancing our work. We'll also provide convincing empirical proofs and a practical demonstration of a VQA application.  Back
 
Topics:
AI & Deep Learning Research, AI Application, Deployment & Inference
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9824
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Abstract:
We'll discuss learning to synthesize object instances such as a person or car in both 2D and 3D scenes. We will introduce our work we presented at NeurIPS 2018 on context-aware synthesis and placement of object instances. We propose a generative mod ...Read More
Abstract:
We'll discuss learning to synthesize object instances such as a person or car in both 2D and 3D scenes. We will introduce our work we presented at NeurIPS 2018 on context-aware synthesis and placement of object instances. We propose a generative model that learns to generate and insert an object instance into an image in a semantically coherent manner. In particular, we represent object instances using masks and learn to insert them into semantic label maps of images. Our talk will also cover our recent work around putting humans in a scene and learning affordance in 3D indoor environments. This extends the learning of context from 2D to 3D scenes in which the synthesized objects are semantically coherent and geometrically correct. We'll show that both projects add technical insights and have potential applications in content creation.  Back
 
Topics:
AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9959
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Abstract:
Join a special presentation from our 2018-2019 Graduate Fellowship recipients to learn what's next from the world of research and academia. Sponsored projects involve a variety of technical challenges, including topics such as 3D scene understanding ...Read More
Abstract:
Join a special presentation from our 2018-2019 Graduate Fellowship recipients to learn what's next from the world of research and academia. Sponsored projects involve a variety of technical challenges, including topics such as 3D scene understanding, new programming models for tensor computations, HPC physics simulations for astrophysics, deep learning algorithms for AI natural language learning, and cancer diagnosis. We believe that theses students will lead the future in our industry and we're proud to support the 2018-2019 NVIDIA Graduate Fellows. For more information on the NVIDIA Graduate Fellowship program, visit www.nvidia.com/en-us/research/graduate-fellowships.  Back
 
Topics:
AI & Deep Learning Research, Virtual Reality & Augmented Reality, Graphics and AI, Computational Biology & Chemistry, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9976
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Abstract:
We will cover the techniques for training DNNs with Tensor Cores described in "S8923 - Training Neural Networks with Mixed Precision: Theory and Practice". These methods were introduced for AI processing with the Volta GPU architecture. T ...Read More
Abstract:
We will cover the techniques for training DNNs with Tensor Cores described in "S8923 - Training Neural Networks with Mixed Precision: Theory and Practice". These methods were introduced for AI processing with the Volta GPU architecture. Tensor Cores provide up to 120 TFlops throughput, mixing operations on IEEE half- and single-precision floats. Techniques used will include loss-scaling, master weights copy, and choosing the proper precision for a given operation. For each of TensorFlow and PyTorch we will describe a fp32 network definition and then demonstrate the same network using mixed precision techniques.  Back
 
Topics:
AI & Deep Learning Research, Algorithms & Numerical Techniques
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S81012
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Abstract:
This tutorial will cover the issues encountered when deploying NVIDIA DGX-1/DGXStation into secure environment. For security reasons, some installations require that systems be isolated from the internet or outside networks. Since most DGX-1 softwar ...Read More
Abstract:
This tutorial will cover the issues encountered when deploying NVIDIA DGX-1/DGXStation into secure environment. For security reasons, some installations require that systems be isolated from the internet or outside networks. Since most DGX-1 software updates are accomplished through an over-the-network process with NVIDIA servers, this session will walk the participants through how updates can be made by maintaining an intermediary server. This session will be a combination of lecture, live demos and along with detailed instructions.  Back
 
Topics:
AI & Deep Learning Research, Data Center & Cloud Infrastructure
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8568
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Accelerated Data Science, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8668
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, 5G & Edge
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8791
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Abstract:
We'll introduce the basic concept of domain adaptation and how to use adversarial training to achieve unsupervised domain adaptation. We'll then describe how the technique is used in two tasks: improving semantic segmentation across cities, and tr ...Read More
Abstract:
We'll introduce the basic concept of domain adaptation and how to use adversarial training to achieve unsupervised domain adaptation. We'll then describe how the technique is used in two tasks: improving semantic segmentation across cities, and transferring language style for image captioning. In particular, we combine domain adaptation with policy gradient-based reinforcement learning approach to transfer language style. The details and results of both tasks are published in ICCV 2017.  Back
 
Topics:
AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8200
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Computational Physics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8826
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8191
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Deep Learning & AI Frameworks, Data Center & Cloud Infrastructure
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8497
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Deep Learning & AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8977
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Abstract:
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
Abstract:

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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Topics:
AI & Deep Learning Research, 5G & Edge, Federal
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8267
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Graphics and AI, Rendering & Ray Tracing, Real-Time Graphics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8453
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Abstract:
We'll introduce GPU-accelerated unsupervised reinforcement and auxiliary learning (UNREAL) algorithm. Recent state-of-the-art deep reinforcement learning algorithms, such as A3C and UNREAL, are designed to train on a single device with only CPUs. Us ...Read More
Abstract:
We'll introduce GPU-accelerated unsupervised reinforcement and auxiliary learning (UNREAL) algorithm. Recent state-of-the-art deep reinforcement learning algorithms, such as A3C and UNREAL, are designed to train on a single device with only CPUs. Using GPU acceleration for these algorithms results in low GPU utilization, which means the full performance of the GPU is not reached. Motivated by the architecture changes made by the GA3C algorithm, which gave A3C better GPU acceleration, together with the high learning efficiency of the UNREAL algorithm, we extend GA3C with the auxiliary tasks from UNREAL to create GUNREAL. We show that our GUNREAL system finished training faster than UNREAL and reached higher scores than GA3C.  Back
 
Topics:
AI & Deep Learning Research, Performance Optimization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8219
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Abstract:
To acquire rich repertoires of skills, robots must be able to learn from their own autonomously collected data. We'll describe a video-prediction model that predicts what a robot will see next, and show how this model can be used to solve co ...Read More
Abstract:

To acquire rich repertoires of skills, robots must be able to learn from their own autonomously collected data. We'll describe a video-prediction model that predicts what a robot will see next, and show how this model can be used to solve complex manipulations tasks in real-world settings. Our model was trained on 44,000 video sequences, where the manipulator autonomously pushes various objects. Using the model, the robot is capable of moving objects that were not seen during training to desired locations, handling multiple objects and pushing objects around obstructions. Unlike other methods in robotic learning, video-prediction does not require any human labels. Our experiments show that the method achieves a significant advance in the range and complexity of skills that can be performed entirely with self-supervised robotic learning. This session is for attendees that possess a basic understanding of convolutional and recurrent neural networks.

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Topics:
AI & Deep Learning Research, Intelligent Machines, IoT & Robotics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8629
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Abstract:
We'll discuss how we could use deep generative modeling in two application domains; in speech synthesis, and in sensor data modeling. We'll give an overview of what generative modeling is and how it could be used for practical AI tasks through the ...Read More
Abstract:
We'll discuss how we could use deep generative modeling in two application domains; in speech synthesis, and in sensor data modeling. We'll give an overview of what generative modeling is and how it could be used for practical AI tasks through these examples. We'll also give a flavor of latent space methods, which we can use to learn more about our data so as to transform them in meaningful ways, with uses in both reconstruction and in generation.  Back
 
Topics:
AI & Deep Learning Research, Advanced AI Learning Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8617
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Speech & Language Processing, Advanced AI Learning Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8151
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Advanced AI Learning Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8581
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Advanced AI Learning Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8609
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Abstract:
We'll discuss ongoing work at NVIDIA on deep active learning. Attendees can expect to learn what active learning is and some of the challenges of applying it to deep neural network training.
 
Topics:
AI & Deep Learning Research, Advanced AI Learning Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8692
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Abstract:
We'll introduce a GAN-based framework for unsupervised image-to-image translation. It leverages a shared latent space assumption to learn to translate an image in one domain to a corresponding image in another domain without requiring any pair of co ...Read More
Abstract:
We'll introduce a GAN-based framework for unsupervised image-to-image translation. It leverages a shared latent space assumption to learn to translate an image in one domain to a corresponding image in another domain without requiring any pair of corresponding images in the two domains in the training dataset. We'll show examples on translating street scene images, from sunny day to rainy day or from day time to night time. We also show image translation results on dog breed conversions and cat species conversion as well as human face translation based on attributes.  Back
 
Topics:
AI & Deep Learning Research, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8114
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Abstract:
Reinforcement learning aims to determine a mapping from observations to actions that maximize a reward criterion. The agent starts off exploring the environment for rewards with random search, which is only likely to succeed in all but simplest ...Read More
Abstract:

Reinforcement learning aims to determine a mapping from observations to actions that maximize a reward criterion. The agent starts off exploring the environment for rewards with random search, which is only likely to succeed in all but simplest of settings. Furthermore, measuring and designing reward functions for real-world tasks is non-trivial. Inspired by research in developmental psychology, in this talk I will discuss how reinforcement learning agents might use curiosity and knowledge accumulated from experience for efficient exploration. I will present results illustrating an agent learning to play the game of Mario and learning to navigate without rewards, a study quantifying the kinds of prior knowledge used by humans for efficient exploration and some robotic manipulation experiments including the use of an anthropomorphic hand for grasping objects. 

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Topics:
AI & Deep Learning Research, Intelligent Machines, IoT & Robotics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8217
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Abstract:
To convert phonemes of telephone conversations and responses at meetings into texts in real time, pass the text to the computational model created by DGX-1, label with a learning without teacher, and add the clusters, we are developing a system ...Read More
Abstract:

To convert phonemes of telephone conversations and responses at meetings into texts in real time, pass the text to the computational model created by DGX-1, label with a learning without teacher, and add the clusters, we are developing a system which compares objects and analyzes meaning of conversation and profiles of interlocutors. With this technology, customers can receive appropriate responses at the beginning of a conversation with a help desk, and patients can receive correspondence during a remote diagnosis with a doctor based solely off of their dialogue and examination results. By using TensorFlow as a platform and running the K-Means method, Word2vec, Doc2Vec, etc. in DGX-1 clustered environment on DGX-1, the result of arithmetic processing is found at high speed conversation. Even if the amount of sentences is increased, the learning effect increases linearly, demonstrating that the proportion of validity can be raised without taking grammar of languages ??other than English (e.g. Japanese) into account.

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Topics:
AI & Deep Learning Research, Speech & Language Processing, AI Startup
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8371
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Abstract:
Building intelligent agents that possess the ability to perceive the rich visual environment around us, communicate this understanding in natural language to humans and other agents, and execute actions in a physical environment, has been a long-term ...Read More
Abstract:
Building intelligent agents that possess the ability to perceive the rich visual environment around us, communicate this understanding in natural language to humans and other agents, and execute actions in a physical environment, has been a long-term goal of Artificial Intelligence. In this talk, I will present my recent work on an instantiation of this goal -- Embodied Question Answering (EQA) -- where an agent that is spawned at a random location in an environment (a house or building) is asked a natural language question ("What color is the car?"). The agent perceives its environment through first-person vision and can perform a few 'atomic' actions: move-{forward, backward, right, left}, and turn-{right, left}. The objective of the agent is to explore the environment and gather visual information necessary to answer the question ("orange"). I'll introduce our OpenGL-based environments, a large-scale dataset of expert demonstrations for this task and deep models, trained end-to-end using reinforcement learning, from raw pixels to multi-step navigation control to visual question answering.  Back
 
Topics:
AI & Deep Learning Research, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8582
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Deep Learning & AI Frameworks, HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8152
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8281
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Abstract:
We are witnessing unprecedented advances in computer vision and AI. What lies next for AI? We believe that the next generation of intelligent systems (say the next generation of Google's Assistant, Facebook's M, Apple's Siri, Amazon's Alexa) will ...Read More
Abstract:
We are witnessing unprecedented advances in computer vision and AI. What lies next for AI? We believe that the next generation of intelligent systems (say the next generation of Google's Assistant, Facebook's M, Apple's Siri, Amazon's Alexa) will need to possess the ability to perceive their environment (through vision, audition, or other sensors), communicate (i.e., hold a natural language dialog with humans and other agents), and act (e.g., aid humans by executing API calls or commands in a virtual or embodied environment), for tasks such as: aiding visually impaired users in understanding their surroundings; interacting with an AI assistant (Human: 'Alexa can you see the baby in the baby monitor?', AI: 'Yes, I can', Human: 'Is he sleeping or playing?'); robotics applications (e.g. search and rescue missions) where the operator may be situationally blind and operating via language. We'll present work from our lab on a range of projects on such visually grounded conversational agents.  Back
 
Topics:
AI & Deep Learning Research, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8571
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Abstract:
In this session we present a Kubernetes deployment on Amazon AWS GPUs that provide customized computer vision to a large number of users. Reza offers an overview of Matroid's pipeline and demonstrates how to customize computer vision neural network ...Read More
Abstract:
In this session we present a Kubernetes deployment on Amazon AWS GPUs that provide customized computer vision to a large number of users. Reza offers an overview of Matroid's pipeline and demonstrates how to customize computer vision neural network models in the browser, followed by building, training, and visualizing TensorFlow models, which are provided at scale to monitor video streams.  Back
 
Topics:
AI & Deep Learning Research, Data Center & Cloud Infrastructure, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8610
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Abstract:
We'll explain the concept and the importance of audio recognition, which aims to understand literally all the information contained in the audio, not limiting its scope to speech recognition. It includes the introduction of various type ...Read More
Abstract:

We'll explain the concept and the importance of audio recognition, which aims to understand literally all the information contained in the audio, not limiting its scope to speech recognition. It includes the introduction of various types of non-verbal information contained in the audio such as acoustic scenes/events, speech, and music. This session is helpful to the people who are not familiar with audio processing but are interested in the context-aware system. Also, it might be inspiring for someone who develops AI applications such as AI home assistant, a humanoid robot, and self-driving cars. It also covers the potential use-cases and creative applications, including a video demonstration of the audio context-aware system applied to media-art performance for real-time music generation.

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Topics:
AI & Deep Learning Research, Speech & Language Processing, AI Startup, GIS
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8696
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Abstract:
We'll present how deep reinforcement learning (DRL) and memory extended networks can be used to train agents, which optimize asset allocations or propose trading actions. The memory component is crucial for improved mini-batch parallelization and he ...Read More
Abstract:
We'll present how deep reinforcement learning (DRL) and memory extended networks can be used to train agents, which optimize asset allocations or propose trading actions. The memory component is crucial for improved mini-batch parallelization and helps mitigate catastrophic forgetting. We also address how concepts from risk-sensitive and safe reinforcement learning apply to improve the robustness of the learned policies. The DRL approach has several advantages over the industry standard approach, which is still based on the mean variance portfolio optimization. The most significant benefit is that the information bottleneck between the statistical return model and the portfolio optimizer is removed, and available market data and trade history are used much more efficiently.  Back
 
Topics:
AI & Deep Learning Research, Algorithms & Numerical Techniques, Advanced AI Learning Techniques, Finance
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8679
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Abstract:
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. ...Read More
Abstract:

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.

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Topics:
AI & Deep Learning Research, Intelligent Machines, IoT & Robotics, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8132
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Abstract:
We'll cover the four known methods for emotion detection: vision, speech, sentiment analysis, and wearable technology. We'll provide a quick dive through each presented solution, and then introduce a novel approach aimed for the future of autonomou ...Read More
Abstract:
We'll cover the four known methods for emotion detection: vision, speech, sentiment analysis, and wearable technology. We'll provide a quick dive through each presented solution, and then introduce a novel approach aimed for the future of autonomous vehicles.  Back
 
Topics:
AI & Deep Learning Research, Consumer Engagement & Personalization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8352
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Virtual Reality & Augmented Reality, Graphics and AI, Computational Biology & Chemistry, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8793
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Abstract:
Estimation of 3D motion in a dynamic scene from a pair of images is a core task in many scene understanding problems. In real world applications, a dynamic scene is commonly captured by a moving camera (i.e., panning, tilting or hand-held), increasin ...Read More
Abstract:
Estimation of 3D motion in a dynamic scene from a pair of images is a core task in many scene understanding problems. In real world applications, a dynamic scene is commonly captured by a moving camera (i.e., panning, tilting or hand-held), increasing the task complexity because the scene is observed from different viewpoints. The main challenge is the disambiguation of the camera motion from scene motions, which becomes more difficult as the amount of rigid parts observed decreases. In this talk, We introduce a method to learn a rigidity of a scene from a large collection of dynamic scene data, and directly infer a rigidity mask from two sequential RGB-D images in a supervised manner. With the learned network, we show how we can effectively estimate camera motion and projected scene flow using computed 2D optical flow and the inferred rigidity mask. Through evaluations, we show that our methods can make the scene flow estimation more robust and stable over state-of-the-art methods in challenging dynamic scenes. The expected audiences will include people who are interested in computer vision algorithms, but not limited to any audiences interested in AI and machine learning in general. We'll cover: the motivation behind scene flow estimation, potential applications, how we train two networks for the scene flow estimation, and how we evaluate the algorithm with popular benchmark dataset, SINTEL. We'll also show a new semi-synthetic dataset and its generation method where we mix real video footage with virtually rendered foreground scenes.  Back
 
Topics:
AI & Deep Learning Research, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8798
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Product & Building Design, Intelligent Video Analytics, GIS, Autonomous Vehicles
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8156
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8924
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Abstract:
Constructing an accurate prediction model from limited data is one of the important tasks in machine learning. We'll introduce unsupervised domain adaptation and a learning method using interclass patterns as a method to construct accurate predictio ...Read More
Abstract:
Constructing an accurate prediction model from limited data is one of the important tasks in machine learning. We'll introduce unsupervised domain adaptation and a learning method using interclass patterns as a method to construct accurate prediction models from limited data. Regarding unsupervised domain adaptation, we use three networks asymmetrically. Two networks are used to label unlabeled target patterns, and one network is trained by the pseudo-labeled patterns to obtain target-discriminative representations. About the learning method using interclass patterns, we generate interclass patterns by mixing two patterns belonging to different classes with a random ratio and train the model to output the mixing ratio form the mixed patterns. Although the algorithm is very simple, the proposed method significantly improves classification performance on sound recognition and image recognition. In addition, we'll briefly introduce various topics, including WebDNN, which our team is working on.  Back
 
Topics:
AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8786
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Advanced AI Learning Techniques, Video & Image Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8784
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Abstract:
Maps are a key component in image-based camera localization and visual SLAM systems: they are used to establish geometric constraints between images, correct drift in relative pose estimation, and relocalize cameras after lost tracking. The exact def ...Read More
Abstract:
Maps are a key component in image-based camera localization and visual SLAM systems: they are used to establish geometric constraints between images, correct drift in relative pose estimation, and relocalize cameras after lost tracking. The exact definitions of maps, however, are often application-specific and hand-crafted for different scenarios (e.g., 3D landmarks, lines, planes, bags of visual words). We propose to represent maps as a deep neural net called MapNet, which enables learning a data-driven map representation. Unlike prior work on learning maps, MapNet exploits cheap and ubiquitous sensory inputs like visual odometry and GPS in addition to images and fuses them together for camera localization. Geometric constraints expressed by these inputs, which have traditionally been used in bundle adjustment or pose-graph optimization, are formulated as loss terms in MapNet training and also used during inference. In addition to directly improving localization accuracy, this allows us to update the MapNet (i.e., maps) in a self-supervised manner using additional unlabeled video sequences from the scene.  Back
 
Topics:
AI & Deep Learning Research, Autonomous Vehicles, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8792
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Abstract:
Densely connected neural networks were originally introduced to avoid the problem of layer-wise vanishing gradients when CNNs are stacked in a very deep fashion, specifically for image recognition tasks. Inspired by these works, we've explored the u ...Read More
Abstract:
Densely connected neural networks were originally introduced to avoid the problem of layer-wise vanishing gradients when CNNs are stacked in a very deep fashion, specifically for image recognition tasks. Inspired by these works, we've explored the use of dense networks connections within LSTM models for the task of automatic speech recognition. By introducing additional connections, to connect (almost) every layer to at least one other layer, we mitigate the vanishing gradient effect between LSTM layers and enable error signals to propagated back to the very first layer during training. In this presentation, we'll present the fundamentals of speech recognition and introduce different neural network model structures that have been shown to be effective for this task. We'll then introduce identity, highway, and dense connections and demonstrate how they improve the performance of these models. We'll evaluate the performance of these models across different datasets, and show that with a lattice-based system combination, densely connected LSTMs significantly contributed to reaching the marks of 5.0% and 9.1% in word error rate (WER) for the Switchboard and CallHome testsets.  Back
 
Topics:
AI & Deep Learning Research, Speech & Language Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8903
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Deep Learning & AI Frameworks, Consumer Engagement & Personalization, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8684
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Abstract:
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
Abstract:

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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Topics:
AI & Deep Learning Research, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8406
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Abstract:
Using only randomized simulated images, we'll present a system to infer and simply execute a human-readable robotic program after watching a real-world task demonstration. The system is comprised of a series of deep neural network modules, e ...Read More
Abstract:

Using only randomized simulated images, we'll present a system to infer and simply execute a human-readable robotic program after watching a real-world task demonstration. The system is comprised of a series of deep neural network modules, each learned entirely in simulation. During training, images are generated in a gaming engine and made transferable to the real world by domain randomization. After training, the system is straightforwardly deployed on a real robot with no retuning of the neural networks and having never previously seen a real image. We demonstrate the system on a Baxter robot performing block tower construction tasks.

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Topics:
AI & Deep Learning Research, Intelligent Machines, IoT & Robotics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8439
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Abstract:
Learn how to generate long answers for non-factoid questions in quality assurance community sites by using the encoder-decoder framework. We'll present our novel extension of the encoder-decoder framework, called the ensemble network, that goes beyo ...Read More
Abstract:
Learn how to generate long answers for non-factoid questions in quality assurance community sites by using the encoder-decoder framework. We'll present our novel extension of the encoder-decoder framework, called the ensemble network, that goes beyond a single short sentence. It handles several sentences (i.e. two major sentence types that organize answers for non-factoid questions, conclusion statements, and its supplementary ones) to generate complicated non-factoid answers.  Back
 
Topics:
AI & Deep Learning Research, Speech & Language Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8301
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Abstract:
Robust object tracking requires knowledge and understanding of the object being tracked: its appearance, motion, and change over time. A tracker must be able to modify its underlying model and adapt to new observations. We present Re3, a real-ti ...Read More
Abstract:

Robust object tracking requires knowledge and understanding of the object being tracked: its appearance, motion, and change over time. A tracker must be able to modify its underlying model and adapt to new observations. We present Re3, a real-time deep object tracker capable of incorporating temporal information into its model. Rather than focusing on a limited set of objects or training a model at test-time to track a specific instance, we pretrain our generic tracker on a large variety of objects and efficiently update on the fly; Re3 simultaneously tracks and updates the appearance model with a single forward pass. This lightweight model is capable of tracking objects at 150 FPS, while attaining competitive results on challenging benchmarks. We also show that our method handles temporary occlusion better than other comparable trackers using experiments that directly measure performance on sequences with occlusion.

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Topics:
AI & Deep Learning Research, Intelligent Video Analytics, Intelligent Machines, IoT & Robotics, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8298
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Abstract:
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 ...Read More
Abstract:

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.  

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Topics:
AI & Deep Learning Research, AI Startup, Deep Learning & AI Frameworks, HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8889
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Abstract:
To effectively leverage the progress in Artificial Intelligence (AI) to make our lives more productive, it is important for humans and AI to work well together in a team. Traditionally, research has focused primarily on making AI more accurate, and ( ...Read More
Abstract:
To effectively leverage the progress in Artificial Intelligence (AI) to make our lives more productive, it is important for humans and AI to work well together in a team. Traditionally, research has focused primarily on making AI more accurate, and (to a lesser extent) on having it better understand human intentions, tendencies, beliefs, and contexts. The latter involves making AI more human-like and having it develop a theory of our minds. In this talk, I will argue that for human-AI teams to be effective, humans must also develop a Theory of AI''s Mind get to know its strengths, weaknesses, beliefs, and quirks. I will present some (very) initial results in the context of visual question answering and visual dialog where the AI agent is trained to answer natural language questions about images.  Back
 
Topics:
AI & Deep Learning Research, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8560
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8242
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Abstract:
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
Abstract:

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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Topics:
AI & Deep Learning Research, Autonomous Vehicles
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8626
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Abstract:
Learn how to predict a dense depth image from a sparse set of depth measurements and a single RGB image. This approach can be applied to serve as a plug-in module in simultaneous localization and mapping to convert sparse maps to dense maps, and as a ...Read More
Abstract:
Learn how to predict a dense depth image from a sparse set of depth measurements and a single RGB image. This approach can be applied to serve as a plug-in module in simultaneous localization and mapping to convert sparse maps to dense maps, and as a super-resolution of LiDAR depth data. We''ll describe the performance of our prediction method, explain how to train the depth prediction network, and showcase examples of its applications. Codes and video demonstration are also publicly available. This session is for registrants who are already familiar with basic machine learning techniques.  Back
 
Topics:
AI & Deep Learning Research, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8216
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Abstract:
We''ll present a framework that can learn a compute-intensive deep neural networks (DNNs) task using multiple AI blocks and evolve better confidence by combining estimates. We''ll consider the example of establishing the identity of a user using spee ...Read More
Abstract:
We''ll present a framework that can learn a compute-intensive deep neural networks (DNNs) task using multiple AI blocks and evolve better confidence by combining estimates. We''ll consider the example of establishing the identity of a user using speech and image data. The system consists of two blocks - the AI block and Arbiter block. The AI block uses multiple DNNs (voice-based and image-based DNNs that generate a low confidence estimate initially). These AI blocks assist each other using Arbiter blocks and build confidence, improve accuracy, and learn salient features over time. Arbiter can store recent unacquainted data at run time in noisy and distorted environments and train the AI blocks periodically or on an on-demand basis. This concept could potentially improve the automatic speech recognition capabilities and allow detection of faces even when variable features of faces change with time. The GPU is the ideal choice as the task requires inferencing as well as training on the go.  Back
 
Topics:
AI & Deep Learning Research, Intelligent Video Analytics, Advanced AI Learning Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8331
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Abstract:
We have long envisioned that machines one day can perform human-like perception, reasoning, and expression across multiple modalities including vision and language, which will augment and transform the ways humans communicate with each other and with ...Read More
Abstract:
We have long envisioned that machines one day can perform human-like perception, reasoning, and expression across multiple modalities including vision and language, which will augment and transform the ways humans communicate with each other and with the real world. With this vision, we''ll introduce the latest work of developing a deep attention GAN for fine-grained language-to-image synthesis. We''ll discuss the open problems behind the task that we''re thrilled to solve, including image and language understanding, joint reasoning across both modalities, and expressing abstract concepts into full imagination, which are of fundamental importance to reaching general intelligence.  Back
 
Topics:
AI & Deep Learning Research, Advanced AI Learning Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8867
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Abstract:
We find 99.9 percent of the gradient exchange in distributed SGD is redundant, and we propose deep gradient compression (DGC) to greatly reduce the communication bandwidth and improve the scalability of distributed training. To preserve accuracy duri ...Read More
Abstract:
We find 99.9 percent of the gradient exchange in distributed SGD is redundant, and we propose deep gradient compression (DGC) to greatly reduce the communication bandwidth and improve the scalability of distributed training. To preserve accuracy during this compression, DGC employs four methods: momentum correction, local gradient clipping, momentum factor masking, and warm-up training. We have applied DGC to image classification, speech recognition, and language modeling with multiple datasets including Cifar10, ImageNet, Penn Treebank, and Librispeech Corpus. In all these scenarios, DGC achieves a gradient compression ratio from 270x to 600x without losing accuracy, cutting the gradient size of ResNet-50 from 97MB to 0.35MB, and for DeepSpeech from 488MB to 0.74MB. DGC enables large-scale distributed training on inexpensive commodity 1Gbps Ethernet and facilitates distributed training on mobile.  Back
 
Topics:
AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8607
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Abstract:
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
Abstract:

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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Topics:
AI & Deep Learning Research, Consumer Engagement & Personalization, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S81011
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Deep Learning & AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8479
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Abstract:
The growth in density of housing in cities like London and New York has resulted in the higher demand for efficient smaller apartments. These designs challenge the use of space and function while trying to ensure the occupants have the perceptio ...Read More
Abstract:

The growth in density of housing in cities like London and New York has resulted in the higher demand for efficient smaller apartments. These designs challenge the use of space and function while trying to ensure the occupants have the perception of a larger space than provided. The process of designing these spaces has always been the responsibility and perception of a handful of designers using 2D and 3D static platforms as part of the overall building design and evaluation, typically constraint by a prescriptive program and functional requirement. A combination of human- and AI-based agents creating and testing these spaces through design and virtual immersive environments (NVIDIA Holodeck) will attempt to ensure the final results are efficient and best fit for human occupancy prior to construction.

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Topics:
AI & Deep Learning Research, Virtual Reality & Augmented Reality
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8398
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Abstract:
End-to-end learning is a powerful new strategy for training neural networks from perception to control. While such systems have been shown to perform well for reactionary control, the representation learned is not usable for higher level decision mak ...Read More
Abstract:
End-to-end learning is a powerful new strategy for training neural networks from perception to control. While such systems have been shown to perform well for reactionary control, the representation learned is not usable for higher level decision making, such as navigation. We''ll discuss the latest methodologies for training end-to-end systems for parallel autonomy, and demonstrate some of the shortcomings when such decision making capability is needed.  Back
 
Topics:
AI & Deep Learning Research, Autonomous Vehicles
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8605
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Abstract:
Driver monitoring systems are used to detect many driver attributes like gaze, head pose, eye openness, and other features pertaining to attention and assistance. We''ll present a synthetic method of generating data for training DNNs, which caters to ...Read More
Abstract:
Driver monitoring systems are used to detect many driver attributes like gaze, head pose, eye openness, and other features pertaining to attention and assistance. We''ll present a synthetic method of generating data for training DNNs, which caters to the above mentioned features of the subject. We use blender for generating synthetic images, powered by NVIDIA GPUs, which can be scaled to match training needs. Synthetic data generatation allows precise control over data points that are difficult to control in a real environment, like pupil dialation. This approach avoids noisy measurements and results in high accuracy without the need for a high-precision 3D sensor.  Back
 
Topics:
AI & Deep Learning Research, Autonomous Vehicles, Advanced AI Learning Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8324
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Abstract:
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
Abstract:

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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Topics:
AI & Deep Learning Research, Intelligent Video Analytics, Intelligent Machines, IoT & Robotics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8260
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Advanced AI Learning Techniques, HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S81001
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Abstract:
Deep reinforcement learning (deep RL) has emerged as a promising direction for autonomous acquisition of complex behaviors due to its ability to process complex sensory input and to acquire elaborate behavior skills, using general-purpose neural netw ...Read More
Abstract:
Deep reinforcement learning (deep RL) has emerged as a promising direction for autonomous acquisition of complex behaviors due to its ability to process complex sensory input and to acquire elaborate behavior skills, using general-purpose neural network representations. Since learning expressive function approximators requires large quantities of data, deep RL has been mostly applied to simulated domains, such as video games and simulated robotic locomotion and manipulation tasks, where the data collection can occur faster than real time and be trivially parallelized. We''ll address techniques that have been proposed to enable deep RL for real-world robotics, and discuss how the maximum-entropy principle can be leveraged to reduce the required amount of real-world interaction.  Back
 
Topics:
AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8603
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Speakers:
Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8234
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Abstract:
We''ll introduce several attempts for modeling the long-term sequence dependence to help improve the action recognition performance. First, we''ll introduce a fused feature of deep and hand-crafted features to prove the complementation between them. ...Read More
Abstract:
We''ll introduce several attempts for modeling the long-term sequence dependence to help improve the action recognition performance. First, we''ll introduce a fused feature of deep and hand-crafted features to prove the complementation between them. We''ll also introduce an attempt of attention model to illustrate the effectiveness of attention mechanism on action recognition. We''ll then introduce shuttleNet, which is a biologically-inspired neural network. Finally, we''ll give some divergent experiments on action recognition to show the potential research direction.  Back
 
Topics:
AI & Deep Learning Research, Computer Vision, Video & Image Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8229
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Abstract:
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
Abstract:

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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Topics:
AI & Deep Learning Research, AI Startup, Deep Learning & AI Frameworks, Autonomous Vehicles
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8758
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Intelligent Video Analytics, Autonomous Vehicles
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8201
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Abstract:
We''ll discuss recent research in deep reinforcement learning (RL), with a focus on the application of intuitions, from planning to neural network architectures for deep RL. Planning in complex visual environments has thus far been held back by the d ...Read More
Abstract:
We''ll discuss recent research in deep reinforcement learning (RL), with a focus on the application of intuitions, from planning to neural network architectures for deep RL. Planning in complex visual environments has thus far been held back by the difficulty of learning accurate predictive models. To address this, we embedded a model inside a differentiable, dynamically-constructed tree-planning architecture, so that we identify an effective model when used within that planner. We''ll share our work on developing these architectures, as well as our approaches to various technical obstacles associated with the efficient optimization of deep tree-structured models on GPU.  Back
 
Topics:
AI & Deep Learning Research, Advanced AI Learning Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8787
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Abstract:
This session will describe an approach to building personalized recommendations using (very) deep autoencoders. We will explore effects of different activation functions, network depth and novel algorithmic approaches. The model is trained end-to-end ...Read More
Abstract:
This session will describe an approach to building personalized recommendations using (very) deep autoencoders. We will explore effects of different activation functions, network depth and novel algorithmic approaches. The model is trained end-to-end without any layer-wise pre-training and our PyTorch-based code is publicly available.  Back
 
Topics:
AI & Deep Learning Research, Consumer Engagement & Personalization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8212
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Abstract:
Learn how VUE.ai''s model generator uses conditional GANs to produce product-specific images suitable for replacing photographs in catalogs. We''ll present networks that generate images of fashion models wearing specific garments ...Read More
Abstract:

Learn how VUE.ai''s model generator uses conditional GANs to produce product-specific images suitable for replacing photographs in catalogs. We''ll present networks that generate images of fashion models wearing specific garments, using an image of the garment as a conditioning variable. Network architecture variants, training, and manipulation of latent variables to control attributes such as model pose, build, or skin color will be addressed.

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Topics:
AI & Deep Learning Research, AI Startup, Advanced AI Learning Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8776
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Computer Vision, Video & Image Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8312
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8899
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Abstract:
Training AI agents that can successfully generalize requires large amounts of diverse labeled training data. Collecting and labeling data is a significant cost in the development of AI applications, which, in some cases, may not even be feasib ...Read More
Abstract:
Training AI agents that can successfully generalize requires large amounts of diverse labeled training data. Collecting and labeling data is a significant cost in the development of AI applications, which, in some cases, may not even be feasible. We'll describe computer graphics facial models that we are developing to generate large labeled synthetic facial data for training deep neural networks. Facial analysis is central to many vision applications that involve human-computer interaction, including robotics, autonomous cars, rehabilitation, and extended usability. Generating and animating human faces with high realism is a well-studied problem in computer graphics; however, very few computer vision AI techniques take advantage of rendered facial data to augment or replace manually collected training data. We'll share key insights of how we successfully use synthetic facial data for training facial analysis classifiers. We'll also demonstrate many sub-tasks on which synthetic data helps to significantly improve accuracy and reduces the need for manual data collection.
 
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Topics:
AI & Deep Learning Research, Intelligent Video Analytics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8794
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Abstract:
We propose a novel computational strategy based on deep and reinforcement learning techniques for de-novo design of molecules with desired properties. This strategy integrates two deep neural networks generative and predictive to generate novel c ...Read More
Abstract:
We propose a novel computational strategy based on deep and reinforcement learning techniques for de-novo design of molecules with desired properties. This strategy integrates two deep neural networks generative and predictive to generate novel chemical structures with the desired properties. In the first phase of the method, generative and predictive models are separately trained with supervised learning algorithms. In the second phase, both models are jointly trained with reinforcement learning approach to bias newly generated chemical structures towards those with desired physical and biological properties. In this proof-of-concept study, we have employed this strategy to design chemical libraries biased toward compounds with either maximal, minimal, or specific range of physical properties, such as melting point and hydrophobicity, as well as to develop novel putative inhibitors of JAK2. This new approach can find a general use for generating targeted chemical libraries optimized for a single desired property or multiple properties.  Back
 
Topics:
AI & Deep Learning Research, Computational Biology & Chemistry
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8254
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Advanced AI Learning Techniques, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8477
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Accelerated Data Science
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8286
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Abstract:
We'll discuss the development of a novel model for video prediction and analysis -- the parallel multi-dimensional long short-term memory (PMD-LSTM). PMD-LSTM is a general model for learning from higher dimensional data such as images, video ...Read More
Abstract:

We'll discuss the development of a novel model for video prediction and analysis -- the parallel multi-dimensional long short-term memory (PMD-LSTM). PMD-LSTM is a general model for learning from higher dimensional data such as images, videos, and biomedical scans. It is an extension of the popular LSTM recurrent neural networks to higher dimensional data with a rearrangement of the recurrent connections to dramatically increase parallelism. This gives the network the ability to compactly model the effect of long-range context in each layer, unlike convolutional networks, which need several layers to cover a larger input context. We'll discuss the blind spot problem in recent work on video prediction, and show how PMD-LSTM based models are fully context-aware for each predicted pixel. These models outperform comparatively complex state-of-the-art approaches significantly in a variety of challenging video prediction scenarios such as car driving, human motion, and diverse human actions.

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Topics:
AI & Deep Learning Research, AI Startup, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8713
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Abstract:
We'll present the results of the SpaceNet 2017-2018 Challenge, preview future SpaceNet Challenges, and how developers can generally access open labeled satellite image training data through SpaceNet on AWS. To date, three SpaceNet Challenges ha ...Read More
Abstract:
We'll present the results of the SpaceNet 2017-2018 Challenge, preview future SpaceNet Challenges, and how developers can generally access open labeled satellite image training data through SpaceNet on AWS. To date, three SpaceNet Challenges have been designed to apply computer vision techniques to satellite imagery which examine building footprint extraction, road network extraction, and off-nadir object detection. SpaceNet on AWS is an online repository of openly available satellite imagery, co-registered map data to train algorithms for developers and data scientists to access for research. This first-of-its-kind open innovation project for the geospatial industry launched in August 2016 as a collaboration between AWS, CosmiQ Works, DigitalGlobe, and NVIDIA. The SpaceNet Roads Challenge, launching in November, builds on labeled training datasets consisting of building footprints across Khartoum, Las Vegas, Paris, and Shanghai by providing over 8,000 km of mapped road networks. It uses a novel metric motivated by graph theory concepts that focused competitors on routing rather than just static road pixel identification.  Back
 
Topics:
AI & Deep Learning Research, GIS
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8553
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Abstract:
We'll cover the theory and practice for training DNNs with Tensor Cores, introduced for AI processing with the Volta GPU architecture. Tensor Cores provide up to 120 TFlops throughput, mixing operations on IEEE half- and single-precision floats. In ...Read More
Abstract:
We'll cover the theory and practice for training DNNs with Tensor Cores, introduced for AI processing with the Volta GPU architecture. Tensor Cores provide up to 120 TFlops throughput, mixing operations on IEEE half- and single-precision floats. In the theory portion of the talk, we'll review the half-precision format, values that arise in DNN computations, and techniques that maximize utilization of fp16 format by these values. Techniques include loss-scaling, master weights, and choosing the proper precision for a given operation. In the practice portion of this talk, we'll survey various models that have been trained in mixed precision, matching the accuracy of fp32 training sessions while using the same hyperparameters. Models include various architectures (feed forward, recurrent, generative) as well as cover diverse tasks (image, speech, and language processing). We'll also provide network design and training guidelines to maximize speed when using Tensor Cores.  Back
 
Topics:
AI & Deep Learning Research, Algorithms & Numerical Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8923
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8672
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S81025
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Abstract:
Recent advances in earth observation are opening up a new exciting area for exploration of satellite image data. We'll teach you how to analyse this new data source with deep neural networks. Focusing on emergency response, you will learn how to app ...Read More
Abstract:
Recent advances in earth observation are opening up a new exciting area for exploration of satellite image data. We'll teach you how to analyse this new data source with deep neural networks. Focusing on emergency response, you will learn how to apply deep neural networks for semantic segmentation on satellite imagery. We will specifically focus on multimodal segmentation and the challenge of overcoming missing modality information during inference time. It is assumed that registrants are already familiar with fundamentals of deep neural networks.  Back
 
Topics:
AI & Deep Learning Research, Advanced AI Learning Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8596
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Graphics and AI, Rendering & Ray Tracing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8788
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Abstract:
We'll showcase how you can apply a wealth of unlabeled image data to significantly improve accuracy and speed of single-shot object-detection (SSD) techniques. Our approach, SSD++, advances the state-of-the-art of single shot multibox-based ...Read More
Abstract:

We'll showcase how you can apply a wealth of unlabeled image data to significantly improve accuracy and speed of single-shot object-detection (SSD) techniques. Our approach, SSD++, advances the state-of-the-art of single shot multibox-based object detectors (such as SSD, YOLO) by employing a novel combination of convolution-deconvolution networks to learn robust feature maps, thus making use of unlabeled dataset, and the fresh approach to have confluence of convolution and deconvolution features to combine generic as well as semantically rich feature maps. As a result, SSD++ drastically reduces the requirement of labeled datasets, works on low-end GPUs, identifies small as well as large objects with high fidelity, and speeds up inference process by decreasing the requirement of default boxes. SSD++ achieves state-of-the-art results on PASCAL VOC and MS COCO datasets. Through ablation study, we'll explain the effectiveness of different components of our architecture that help us achieve improved accuracy on the above datasets. We'll further show a case study of SSD++ to identify shoppable objects in fashion, home decor, and food industry from images in the wild.

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Topics:
AI & Deep Learning Research, AI Startup, Computer Vision, Video & Image Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8159
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Speech & Language Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8542
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Abstract:
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
Abstract:

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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Topics:
AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8456
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Computer Vision, Video & Image Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8311
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Abstract:
Want to get started using TensorFlow together with GPUs? Then come to this session, where we will cover the TensorFlow APIs you should use to define and train your models, and the best practices for distributing the training workloads to multipl ...Read More
Abstract:

Want to get started using TensorFlow together with GPUs? Then come to this session, where we will cover the TensorFlow APIs you should use to define and train your models, and the best practices for distributing the training workloads to multiple GPUs. We will also look at the underlying reasons why are GPUs are so great to use for Machine Learning workloads?

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Topics:
AI & Deep Learning Research, Artificial Intelligence and Deep Learning, Developer Tools
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8946
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Abstract:
Humans are remarkably proficient at controlling their limbs and tools from a wide range of viewpoints, diverse environments and in the presence of distractors. In robotics, this ability is referred to as visual servoing. Standard visual servoing ...Read More
Abstract:

Humans are remarkably proficient at controlling their limbs and tools from a wide range of viewpoints, diverse environments and in the presence of distractors. In robotics, this ability is referred to as visual servoing. Standard visual servoing approaches have limited generalization as they typically rely on manually designed features and calibrated camera. We exhibit generalizable visual servoing in the context of robotic manipulation and navigation tasks learned through visual feedback and by deep reinforcement learning (RL) without needing any calibrated setup. By highly randomizing our simulator, we train policies that generalize to novel environments and also to the challenging real world scenarios. Our domain randomization technique addresses the high sample complexity of deep RL, avoids the dangers of trial-and-error and also provides us with the liberty to learn recurrent vision-based policies for highly diverse tasks where capturing sufficient real robot data is impractical. An example of such scenario is learning view-invariant robotic policies which leads into learning physical embodiment and self-calibration purely through visual feedback.

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Topics:
AI & Deep Learning Research, Intelligent Machines, IoT & Robotics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8955
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Advanced AI Learning Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8391
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Algorithms & Numerical Techniques, HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8807
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, HPC and AI, Medical Imaging & Radiology
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S81033
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Abstract:
Voxelized representation of 3D objects is commonly used for training 3D-Convolutional Neural Networks for object detection and classification. However, high-resolution voxelization of CAD models are memory intensive and hence, it is not possible to l ...Read More
Abstract:
Voxelized representation of 3D objects is commonly used for training 3D-Convolutional Neural Networks for object detection and classification. However, high-resolution voxelization of CAD models are memory intensive and hence, it is not possible to load multiple models in the GPU for training. We have developed a GPU-accelerated voxelization technique that generates multi-level voxel grids of 3D objects. Instead of creating a single high-resolution voxel grid for the whole object, this technique generates selective region-based high-resolution voxel grids to represent detailed features in the object. We have also developed a multi-resolution 3D-Convolutional Neural Network that uses this hybrid voxelization for accurate object recognition and classification.  Back
 
Topics:
AI & Deep Learning Research, Industrial Inspection, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8389
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Abstract:
We'll present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks. Conditional GANs have enabled a variety of applications, but the results are often limited ...Read More
Abstract:
We'll present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks. Conditional GANs have enabled a variety of applications, but the results are often limited to low-res and still far from realistic. We'll show that we're capable of generating 2048x1024 visually appealing results with a novel adversarial loss, as well as new multi-scale generator and discriminator architectures. Furthermore, we extend our framework to interactive visual manipulation with two additional features. First, we incorporate object instance segmentation information, which enables object manipulations such as removing/adding objects and changing the object category. Second, we propose a method to generate diverse results given the same input, allowing users to edit the object appearance interactively. Human opinion studies demonstrate that our method significantly outperforms existing methods, advancing both the quality and the resolution of deep image synthesis and editing.  Back
 
Topics:
AI & Deep Learning Research, Graphics and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8918
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Abstract:
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
Abstract:
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
 
Topics:
AI & Deep Learning Research, Accelerated Data Science
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S81013
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AI Application, Deployment & Inference
Presentation
Media
Abstract:
This talk addresses how enterprises can reduce risk by taking a practical approach with their AI initiatives from conceptualization to deployment by evaluating business value, managing open source effectively, creating development environments that e ...Read More
Abstract:
This talk addresses how enterprises can reduce risk by taking a practical approach with their AI initiatives from conceptualization to deployment by evaluating business value, managing open source effectively, creating development environments that economically scale over time as the needs grow, and creating effective workflows based on data movement. By taking this approach to AI, enterprises customers can make incremental investments for success while overcoming the complexity of this new technology  Back
 
Topics:
AI Application, Deployment & Inference, AI & Deep Learning Business Track (High Level)
Type:
Sponsored Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91000
Streaming: