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GTC On-Demand

AI and DL Research
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Domain Adaptation Using Adversarial Training for Semantic Segmentation and Caption Style Transfer
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.
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
 
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AI and DL Research, GTC Silicon Valley 2018 - ID S8200
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Deep Learning and AI
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High Performance CTC Training for End-to-End Speech Recognition on GPU
End-to-end speech recognition systems, which directly transcribe audio data with text without requiring an intermediate phonetic representation, are based on recurrent neural network (RNN) + connectionist temporal classification (CTC). CTC is to automatically learn the alignments between speech frames and the label sequence of transcript. In this work, we focus on optimizing CTC training, especially the forward-backward algorithm, on GPU. Firstly, opportunities of saving computation and memory access of CTC forward-backward algorithm were quantitatively analyzed and utilized to get a speedup of ~1.28X. Secondly, by data reuse among frames and data transfer between frames through register file and shared memory, we get a speedup of ~1.80X.
End-to-end speech recognition systems, which directly transcribe audio data with text without requiring an intermediate phonetic representation, are based on recurrent neural network (RNN) + connectionist temporal classification (CTC). CTC is to automatically learn the alignments between speech frames and the label sequence of transcript. In this work, we focus on optimizing CTC training, especially the forward-backward algorithm, on GPU. Firstly, opportunities of saving computation and memory access of CTC forward-backward algorithm were quantitatively analyzed and utilized to get a speedup of ~1.28X. Secondly, by data reuse among frames and data transfer between frames through register file and shared memory, we get a speedup of ~1.80X.  Back
 
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Deep Learning and AI, GTC Silicon Valley 2016 - ID S6383
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Deep Learning and AI Frameworks
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Experiences of End2end Deep Learning Optimization on Alibaba PAI Deep Learning Platform
We'll share experiences of end-to-end deep learning optimization on Alibaba's platform of artificial intelligence (PAI), including both offline training and online inference. For offline training, dedicated optimization is made for local and distributed environment. For online inference, the optimization is done through both algorithm and system perspectives. Both the methodology and benchmark number are shared during this session. We'll share several business applications driven by these optimizations to ensure learning to bridge the gap between low-level optimization and real business scenarios.
We'll share experiences of end-to-end deep learning optimization on Alibaba's platform of artificial intelligence (PAI), including both offline training and online inference. For offline training, dedicated optimization is made for local and distributed environment. For online inference, the optimization is done through both algorithm and system perspectives. Both the methodology and benchmark number are shared during this session. We'll share several business applications driven by these optimizations to ensure learning to bridge the gap between low-level optimization and real business scenarios.  Back
 
Keywords:
Deep Learning and AI Frameworks, Performance Optimization, GTC Silicon Valley 2018 - ID S8113
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