Soumith Chintala, AI research engineer, Facebook, at MLconf NYC 2017
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Soumith Chintala is a Researcher at Facebook AI Research, where he works on deep learning, reinforcement learning, generative image models, agents for video games and large-scale high-performance deep learning. Prior to joining Facebook in August 2014, he worked at MuseAmi, where he built deep learning models for music and vision targeted at mobile devices. He holds a Masters in CS from NYU, and spent time in Yann LeCun’s NYU lab building deep learning models for pedestrian detection, natural image OCR, depth-images among others. Abstract Summary: Dynamic Deep Learning: a paradigm shift in AI research and tools: AI research has seen many shifts in the last few years. We’ve seen research go from using static datasets such as Imagenet to being more dynamic and online in self-driving cars, robots and game-playing.Many dynamic environments such as Universe and Starcraft are being used in AI research to solve problems pertaining to reinforcement learning and online learning. In this talk, I shall discuss these shifts in research. Tools such as PyTorch, DyNet and Chainer have popped up to cope up with the paradigm shift, enabling cutting-edge AI, and I shall discuss these as well.
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Soumith Chintala, AI research engineer, Facebook, at MLconf NYC 2017
- 2. Dynamic Deep Learning A paradigm shift in AI research and Tools Soumith Chintala Facebook AI Research
- 3. Today's AI Active Research & Future AI Tools for AI keeping up with change Overview
- 4. Today's AI Future AI Tools for AI Today's AI DenseCap by Justin Johnson & group https://github.com/jcjohnson/densecap
- 5. Today's AI Future AI Tools for AI Today's AI DeepMask by Pedro Pinhero & group
- 6. Today's AI Future AI Tools for AI Today's AI Machine Translation
- 7. Today's AI Future AI Tools for AI Today's AI Text Classification (sentiment analysis etc.) Text Embeddings Graph embeddings Machine Translation Ads ranking
- 8. Data BatchNorm ReLU Conv2d Model ObjectiveTrain Model Today's AI Future AI Tools for AI Today's AI
- 9. Data BatchNorm ReLU Conv2d Model ObjectiveTrain Model BatchNorm ReLU Conv2d Deploy & Use New Data Prediction Today's AI Future AI Tools for AI Today's AI
- 10. Data BatchNorm ReLU Conv2d ObjectiveTrain Model BatchNorm ReLU Conv2d Deploy & Use New Data Prediction Today's AI Future AI Tools for AI Static datasets + Static model structure Today's AI
- 11. Data BatchNorm ReLU Conv2d ObjectiveTrain Model BatchNorm ReLU Conv2d Deploy & Use New Data Prediction Today's AI Future AI Tools for AI Static datasets + Static model structure Offline Learning Today's AI
- 12. Today's AI Future AI Tools for AI Current AI Research / Future AI Self-driving Cars
- 13. Today's AI Future AI Tools for AI Current AI Research / Future AI Agents trained in many environments Cars Video games Internet
- 14. Today's AI Future AI Tools for AI Current AI Research / Future AI Dynamic Neural Networks self-adding new memory or layers changing evaluation path based on inputs
- 15. Today's AI Future AI Tools for AI Current AI Research / Future AI Live data BatchNorm ReLU Conv2d Prediction Continued Online Learning
- 16. Today's AI Future AI Tools for AI Current AI Research / Future AI Sample-1 BatchNorm ReLU Conv2d Prediction Data-dependent change in model structure BatchNorm ReLU Conv2d BatchNorm ReLU Conv2d BatchNorm ReLU Conv2d BatchNorm ReLU Conv2d BatchNorm ReLU Conv2d BatchNorm ReLU Conv2d
- 17. Today's AI Future AI Tools for AI Current AI Research / Future AI Sample-2 BatchNorm ReLU Conv2d Prediction Data-dependent change in model structure BatchNorm ReLU Conv2d BatchNorm ReLU Conv2d BatchNorm ReLU Conv2d BatchNorm ReLU Conv2d BatchNorm ReLU Conv2d
- 18. Today's AI Future AI Tools for AI Current AI Research / Future AI Sample BatchNorm ReLU Conv2d Prediction BatchNorm ReLU Conv2d BatchNorm ReLU Conv2d Change in model-capacity at runtime BatchNorm ReLU Conv2d BatchNorm ReLU Conv2d BatchNorm ReLU Conv2d BatchNorm ReLU Conv2d
- 19. Today's AI Future AI Tools for AI Current AI Research / Future AI Sample BatchNorm ReLU Conv2d Prediction BatchNorm ReLU Conv2d BatchNorm ReLU Conv2d Change in model-capacity at runtime BatchNorm ReLU Conv2d BatchNorm ReLU Conv2d BatchNorm ReLU Conv2d BatchNorm ReLU Conv2d BatchNorm ReLU Conv2d BatchNorm ReLU Conv2d BatchNorm ReLU Conv2d BatchNorm ReLU Conv2d
- 20. Today's AI Future AI Tools for AI A next-gen framework for AI •Interop with many dynamic environments - Connecting to car sensors should be as easy as training on a dataset - Connect to environments such as OpenAI Universe •Dynamic Neural Networks - Change behavior and structure of neural network at runtime •Minimal Abstractions - more complex AI systems means harder to debug without a simple API
- 21. Today's AI Future AI Tools for AI Tools for AI research and deployment Many machine learning tools and deep learning frameworks
- 22. Today's AI Future AI Tools for AI Tools for AI research and deployment Static graph frameworks Dynamic graph frameworks
- 23. Today's AI Future AI Tools for AI Dynamic graph Frameworks •Model is constructed on the fly at runtime •Change behavior, structure of model •Imperative style of programming
- 24. PyTorch Autograd from torch.autograd import Variable
- 25. from torch.autograd import Variable x = Variable(torch.randn(1, 10)) prev_h = Variable(torch.randn(1, 20)) W_h = Variable(torch.randn(20, 20)) W_x = Variable(torch.randn(20, 10)) PyTorch Autograd
- 26. from torch.autograd import Variable x = Variable(torch.randn(1, 10)) prev_h = Variable(torch.randn(1, 20)) W_h = Variable(torch.randn(20, 20)) W_x = Variable(torch.randn(20, 10)) i2h = torch.mm(W_x, x.t()) h2h = torch.mm(W_h, prev_h.t()) MMMM PyTorch Autograd
- 27. from torch.autograd import Variable x = Variable(torch.randn(1, 10)) prev_h = Variable(torch.randn(1, 20)) W_h = Variable(torch.randn(20, 20)) W_x = Variable(torch.randn(20, 10)) i2h = torch.mm(W_x, x.t()) h2h = torch.mm(W_h, prev_h.t()) next_h = i2h + h2h MMMM PyTorch Autograd
- 28. from torch.autograd import Variable x = Variable(torch.randn(1, 10)) prev_h = Variable(torch.randn(1, 20)) W_h = Variable(torch.randn(20, 20)) W_x = Variable(torch.randn(20, 10)) i2h = torch.mm(W_x, x.t()) h2h = torch.mm(W_h, prev_h.t()) next_h = i2h + h2h Add MMMM PyTorch Autograd
- 29. from torch.autograd import Variable x = Variable(torch.randn(1, 10)) prev_h = Variable(torch.randn(1, 20)) W_h = Variable(torch.randn(20, 20)) W_x = Variable(torch.randn(20, 10)) i2h = torch.mm(W_x, x.t()) h2h = torch.mm(W_h, prev_h.t()) next_h = i2h + h2h next_h = next_h.tanh() Add MMMM Tanh PyTorch Autograd
- 30. from torch.autograd import Variable x = Variable(torch.randn(1, 10)) prev_h = Variable(torch.randn(1, 20)) W_h = Variable(torch.randn(20, 20)) W_x = Variable(torch.randn(20, 10)) i2h = torch.mm(W_x, x.t()) h2h = torch.mm(W_h, prev_h.t()) next_h = i2h + h2h next_h = next_h.tanh() next_h.backward(torch.ones(1, 20)) Add MMMM Tanh PyTorch Autograd
- 31. http://pytorch.org Released Jan 18th 20,000+ downloads 200+ community repos 3000+ user posts With ❤ from