Journal ArticleDOI
Mastering the game of Go with deep neural networks and tree search
David Silver,Aja Huang,Chris J. Maddison,Arthur Guez,Laurent Sifre,George van den Driessche,Julian Schrittwieser,Ioannis Antonoglou,Veda Panneershelvam,Marc Lanctot,Sander Dieleman,Dominik Grewe,John Nham,Nal Kalchbrenner,Ilya Sutskever,Timothy P. Lillicrap,Madeleine Leach,Koray Kavukcuoglu,Thore Graepel,Demis Hassabis +19 more
TLDR
Using this search algorithm, the program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0.5, the first time that a computer program has defeated a human professional player in the full-sized game of Go.Abstract:
The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of stateof-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.read more
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Journal ArticleDOI
Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends
Matthew Veres,Medhat Moussa +1 more
TL;DR: This paper presents a survey that highlights the role modeling techniques within the realm of deep learning have played within ITS, focusing on how practitioners have formulated problems to address these various challenges, and outline both architectural and problem-specific considerations used to develop solutions.
Posted Content
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
Tarek R. Besold,Artur S. d'Avila Garcez,Sebastian Bader,Howard Bowman,Pedro Domingos,Pascal Hitzler,Kai-Uwe Kuehnberger,Luis C. Lamb,Daniel Lowd,Priscila M. V. Lima,Leo de Penning,Gadi Pinkas,Hoifung Poon,Gerson Zaverucha +13 more
TL;DR: This joint survey reviews the personal ideas and views of several researchers on neural-symbolic learning and reasoning and presents the challenges facing the area and avenues for further research.
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Spatially Adaptive Computation Time for Residual Networks
Michael Figurnov,Maxwell D. Collins,Yukun Zhu,Li Zhang,Jonathan Huang,Dmitry Vetrov,Ruslan Salakhutdinov +6 more
TL;DR: In this paper, a deep learning architecture based on Residual Network that dynamically adjusts the number of executed layers for the regions of the image is proposed, which is end-to-end trainable, deterministic and problem-agnostic.
Posted Content
A Deep Reinforcement Learning Chatbot
Iulian Vlad Serban,Chinnadhurai Sankar,Mathieu Germain,Saizheng Zhang,Zhouhan Lin,Sandeep Subramanian,Taesup Kim,Michael Pieper,Sarath Chandar,Nan Rosemary Ke,Sai Rajeshwar,Alexandre de Brébisson,Jose Sotelo,Dendi Suhubdy,Vincent Michalski,Alexandre Nguyen,Joelle Pineau,Yoshua Bengio +17 more
TL;DR: MILA's MILABOT is capable of conversing with humans on popular small talk topics through both speech and text and consists of an ensemble of natural language generation and retrieval models, including template-based models, bag-of-words models, sequence-to-sequence neural network and latent variable neural network models.
Journal ArticleDOI
Fp-bnn
TL;DR: FP-BNN, a binarized neural network (BNN) for FPGAs, is presented, which drastically cuts down the hardware consumption while maintaining acceptable accuracy, and an inference performance of Tera opartions per second with acceptable accuracy loss is obtained.
References
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Journal ArticleDOI
Human-level control through deep reinforcement learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
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