Timezone: »
Over the past few years, we have built large-scale computer systems for training neural networks, and then applied these systems to a wide variety of problems that have traditionally been very difficult for computers. We have made significant improvements in the state-of-the-art in many of these areas, and our software systems and algorithms have been used by dozens of different groups at Google to train state-of-the-art models for speech recognition, image recognition, various visual detection tasks, language modeling, language translation, and many other tasks. In this talk,we'll highlight some of the distributed systems and algorithms that we use in order to train large models quickly, and demonstrate TensorFlow (tensorflow.org), an open-source software system we have put together that makes it easy to conduct research in large-scale machine learning.
Author Information
Jeff Dean (Google Brain Team)
Jeff joined Google in 1999 and is currently a Google Senior Fellow. He currently leads Google's Research and Health divisions, where he co-founded the Google Brain team. He has co-designed/implemented multiple generations of Google's distributed machine learning systems for neural network training and inference, as well as multiple generations of Google's crawling, indexing, and query serving systems, and major pieces of Google's initial advertising and AdSense for Content systems. He is also a co-designer and co-implementor of Google's distributed computing infrastructure, including the MapReduce, BigTable and Spanner systems, protocol buffers, LevelDB, systems infrastructure for statistical machine translation, and a variety of internal and external libraries and developer tools. He received a Ph.D. in Computer Science from the University of Washington in 1996, working with Craig Chambers on compiler techniques for object-oriented languages. He is a Fellow of the ACM, a Fellow of the AAAS, a member of the U.S. National Academy of Engineering, and a recipient of the Mark Weiser Award and the ACM Prize in Computing.
Oriol Vinyals (Google)
Oriol Vinyals is a Research Scientist at Google. He works in deep learning with the Google Brain team. Oriol holds a Ph.D. in EECS from University of California, Berkeley, and a Masters degree from University of California, San Diego. He is a recipient of the 2011 Microsoft Research PhD Fellowship. He was an early adopter of the new deep learning wave at Berkeley, and in his thesis he focused on non-convex optimization and recurrent neural networks. At Google Brain he continues working on his areas of interest, which include artificial intelligence, with particular emphasis on machine learning, language, and vision.
More from the Same Authors
-
2020 Poster: Pointer Graph Networks »
Petar Veličković · Lars Buesing · Matthew Overlan · Razvan Pascanu · Oriol Vinyals · Charles Blundell -
2020 Spotlight: Pointer Graph Networks »
Petar Veličković · Lars Buesing · Matthew Overlan · Razvan Pascanu · Oriol Vinyals · Charles Blundell -
2020 Session: Orals & Spotlights Track 28: Deep Learning »
Oriol Vinyals · Guido Montufar -
2019 Poster: Generating Diverse High-Fidelity Images with VQ-VAE-2 »
Ali Razavi · Aaron van den Oord · Oriol Vinyals -
2019 Poster: Classification Accuracy Score for Conditional Generative Models »
Suman Ravuri · Oriol Vinyals -
2018 Poster: Relational recurrent neural networks »
Adam Santoro · Ryan Faulkner · David Raposo · Jack Rae · Mike Chrzanowski · Theophane Weber · Daan Wierstra · Oriol Vinyals · Razvan Pascanu · Timothy Lillicrap -
2017 Workshop: Deep Learning: Bridging Theory and Practice »
Sanjeev Arora · Maithra Raghu · Russ Salakhutdinov · Ludwig Schmidt · Oriol Vinyals -
2017 Poster: Imagination-Augmented Agents for Deep Reinforcement Learning »
Sébastien Racanière · Theophane Weber · David Reichert · Lars Buesing · Arthur Guez · Danilo Jimenez Rezende · Adrià Puigdomènech Badia · Oriol Vinyals · Nicolas Heess · Yujia Li · Razvan Pascanu · Peter Battaglia · Demis Hassabis · David Silver · Daan Wierstra -
2017 Oral: Imagination-Augmented Agents for Deep Reinforcement Learning »
Sébastien Racanière · Theophane Weber · David Reichert · Lars Buesing · Arthur Guez · Danilo Jimenez Rezende · Adrià Puigdomènech Badia · Oriol Vinyals · Nicolas Heess · Yujia Li · Razvan Pascanu · Peter Battaglia · Demis Hassabis · David Silver · Daan Wierstra -
2017 Poster: Neural Discrete Representation Learning »
Aaron van den Oord · Oriol Vinyals · koray kavukcuoglu -
2017 Tutorial: Deep Learning: Practice and Trends »
Nando de Freitas · Scott Reed · Oriol Vinyals -
2016 Poster: Conditional Image Generation with PixelCNN Decoders »
Aaron van den Oord · Nal Kalchbrenner · Lasse Espeholt · koray kavukcuoglu · Oriol Vinyals · Alex Graves -
2016 Poster: An Online Sequence-to-Sequence Model Using Partial Conditioning »
Navdeep Jaitly · Quoc V Le · Oriol Vinyals · Ilya Sutskever · David Sussillo · Samy Bengio -
2016 Poster: Strategic Attentive Writer for Learning Macro-Actions »
Alexander (Sasha) Vezhnevets · Volodymyr Mnih · Simon Osindero · Alex Graves · Oriol Vinyals · John Agapiou · koray kavukcuoglu -
2016 Poster: Matching Networks for One Shot Learning »
Oriol Vinyals · Charles Blundell · Timothy Lillicrap · koray kavukcuoglu · Daan Wierstra -
2015 Poster: Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks »
Samy Bengio · Oriol Vinyals · Navdeep Jaitly · Noam Shazeer -
2015 Poster: Pointer Networks »
Oriol Vinyals · Meire Fortunato · Navdeep Jaitly -
2015 Spotlight: Pointer Networks »
Oriol Vinyals · Meire Fortunato · Navdeep Jaitly -
2015 Poster: Grammar as a Foreign Language »
Oriol Vinyals · Łukasz Kaiser · Terry Koo · Slav Petrov · Ilya Sutskever · Geoffrey Hinton -
2013 Poster: DeViSE: A Deep Visual-Semantic Embedding Model »
Andrea Frome · Greg Corrado · Jon Shlens · Samy Bengio · Jeff Dean · Marc'Aurelio Ranzato · Tomas Mikolov -
2013 Poster: Distributed Representations of Words and Phrases and their Compositionality »
Tomas Mikolov · Ilya Sutskever · Kai Chen · Greg Corrado · Jeff Dean -
2012 Poster: Large Scale Distributed Deep Networks »
Jeff Dean · Greg Corrado · Rajat Monga · Kai Chen · Matthieu Devin · Quoc V Le · Mark Mao · Marc'Aurelio Ranzato · Andrew Senior · Paul Tucker · Ke Yang · Andrew Y Ng