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
Citations
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Journal ArticleDOI
Pulmonary nodule classification with deep residual networks
TL;DR: The proposed method of combining deep residual learning, curriculum learning, and transfer learning translates to high nodule classification accuracy reveals a promising new direction for effective pulmonary nodule CAD systems that mirrors the success of recent deep learning advances in other image-based application domains.
Journal ArticleDOI
Toward Intelligent Vehicular Networks: A Machine Learning Framework
Le Liang,Hao Ye,Geoffrey Ye Li +2 more
TL;DR: This paper identifies the distinctive characteristics of high mobility vehicular networks and motivates the use of machine learning to address the resulting challenges and discusses in greater detail the application of reinforcement learning in managing network resources as an alternative to the prevalent optimization approach.
Journal ArticleDOI
Metal Artifact Reduction in CT: Where Are We After Four Decades?
Lars Gjesteby,Bruno De Man,Yannan Jin,Harald Paganetti,J Verburg,Drosoula Giantsoudi,Ge Wang +6 more
TL;DR: The primary goals of this paper are to identify the strengths and limitations of individual MAR methods and overall classes, and establish a relationship between types of metal objects and the classes that most effectively overcome their artifacts.
Journal ArticleDOI
Development and Arealization of the Cerebral Cortex.
Cathryn R. Cadwell,Aparna Bhaduri,Mohammed A. Mostajo-Radji,Matthew G. Keefe,Tomasz J. Nowakowski +4 more
TL;DR: This work proposes an integrated model of serial homology whereby intrinsic genetic programs and local factors establish early transcriptomic differences between excitatory neurons destined to give rise to broad "proto-regions," and activity-dependent mechanisms lead to progressive refinement and formation of sharp boundaries between functional areas.
Journal ArticleDOI
Three-dimensional memristor circuits as complex neural networks
Peng Lin,Peng Lin,Can Li,Zhongrui Wang,Yunning Li,Hao Jiang,Wenhao Song,Mingyi Rao,Ye Zhuo,Navnidhi K. Upadhyay,Mark Barnell,Qing Wu,Jianhua Yang,Qiangfei Xia +13 more
TL;DR: A three-dimensional circuit composed of eight layers of monolithically integrated memristive devices is built and used to implement complex neural networks, demonstrating accurate MNIST classification and effective edge detection in videos.
References
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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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