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GTC ON-DEMAND

Presentation
Media
Abstract:
This European customer panel brings together A.I. implementers who have deployed deep learning at scale using NVIDIA DGX Systems. We'll focus on specific technical challenges we faced, solution design considerations, and best practices learned from implementing our respective solutions. Attendees will gain insights such as: 1) how to set up your deep learning project for success by matching the right hardware and software platform options to your use case and operational needs; 2) how to design your architecture to overcome unnecessary bottlenecks that inhibit scalable training performance; and 3) how to build an end-to-end deep learning workflow that enables productive experimentation, training at scale, and model refinement.
This European customer panel brings together A.I. implementers who have deployed deep learning at scale using NVIDIA DGX Systems. We'll focus on specific technical challenges we faced, solution design considerations, and best practices learned from implementing our respective solutions. Attendees will gain insights such as: 1) how to set up your deep learning project for success by matching the right hardware and software platform options to your use case and operational needs; 2) how to design your architecture to overcome unnecessary bottlenecks that inhibit scalable training performance; and 3) how to build an end-to-end deep learning workflow that enables productive experimentation, training at scale, and model refinement.  Back
 
Topics:
Artificial Intelligence and Deep Learning
Type:
Panel
Event:
GTC Europe
Year:
2018
Session ID:
E8114
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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 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.
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:

Recent advances in earth observation are opening up a new exciting area for exploration of satellite image data. In this session you will learn how to analyse this new data source with deep neural networks. Focusing on Emergency Response, you will learn (1) how to apply deep neural networks for Semantic Segmentation on satellite imagery. Additionally, we present recent advances of the Multimedia Satellite Task at MediaEval 2017 and show (2) how to extract and fuse content of natural disasters from Satellite Imagery and Social Media Streams. It is assumed that registrants are already familiar with fundamentals of deep neural networks.

Recent advances in earth observation are opening up a new exciting area for exploration of satellite image data. In this session you will learn how to analyse this new data source with deep neural networks. Focusing on Emergency Response, you will learn (1) how to apply deep neural networks for Semantic Segmentation on satellite imagery. Additionally, we present recent advances of the Multimedia Satellite Task at MediaEval 2017 and show (2) how to extract and fuse content of natural disasters from Satellite Imagery and Social Media Streams. It is assumed that registrants are already familiar with fundamentals of deep neural networks.

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Topics:
Other
Type:
Talk
Event:
GTC Europe
Year:
2017
Session ID:
23479
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Abstract:

Generative adversarial networks (GANs) have been applied for multiple cases, such as generating images and image completion. One interesting feature of GANs is the exploration in latent space, where new elements can appear caused by the interpolation between two seed elements. With this in mind, we're interested in exploring latent space in terms of adjective-noun pairs (ANP) able to capture subjectivity in visual content such as "cloudy sky" vs. "pretty sky." Although it is challenging for humans to find a smooth transition between two ANPs (similar to color gradient or color progression), the presented GANs are capable of generating such a gradient in the adjective domain and find new ANPs that lie in this (subjective) transition. As result, GANs offer a more quantified interpretation for this subjective progression and an explainability of the underlying latent space.

Generative adversarial networks (GANs) have been applied for multiple cases, such as generating images and image completion. One interesting feature of GANs is the exploration in latent space, where new elements can appear caused by the interpolation between two seed elements. With this in mind, we're interested in exploring latent space in terms of adjective-noun pairs (ANP) able to capture subjectivity in visual content such as "cloudy sky" vs. "pretty sky." Although it is challenging for humans to find a smooth transition between two ANPs (similar to color gradient or color progression), the presented GANs are capable of generating such a gradient in the adjective domain and find new ANPs that lie in this (subjective) transition. As result, GANs offer a more quantified interpretation for this subjective progression and an explainability of the underlying latent space.

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Topics:
Artificial Intelligence and Deep Learning, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7608
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Abstract:

According to PricewaterhouseCooper's "Global Economic Crime Survey 2014", 37% of the 5,128 organizations surveyed had been victims of economic crime. Given this rise in economic crimes, investigators are working on novel forensic methods to detect financial fraud. Traditional data driven fraud detection methods are only capable of detecting transactions that correspond to already known fraud schemes. An open question is how to detect multi-dimensional patterns indicating 'anomalous' transactions in very large volumes of accounting data. This talk will present PricewaterhouseCooper's and the DFKI's recently developed approach to detect anomalous journal entries / transactions in large scale financial and accounting data by the use of deep learning and stacked autoencoders. 

According to PricewaterhouseCooper's "Global Economic Crime Survey 2014", 37% of the 5,128 organizations surveyed had been victims of economic crime. Given this rise in economic crimes, investigators are working on novel forensic methods to detect financial fraud. Traditional data driven fraud detection methods are only capable of detecting transactions that correspond to already known fraud schemes. An open question is how to detect multi-dimensional patterns indicating 'anomalous' transactions in very large volumes of accounting data. This talk will present PricewaterhouseCooper's and the DFKI's recently developed approach to detect anomalous journal entries / transactions in large scale financial and accounting data by the use of deep learning and stacked autoencoders. 

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Topics:
Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Europe
Year:
2016
Session ID:
SEU6167
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