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
Memristive Synapses and Neurons for Bioinspired Computing
Rui Yang,He-Ming Huang,Xin Guo +2 more
TL;DR: Approaches to realize certain synaptic or neuronal functions are introduced with state‐of‐art experimental demonstrations and approaches to realize the important learning rules, like spiking‐timing‐dependent plasticity and Bienenstock–Cooper–Munro learning rules are elaborated according to the level of faithfulness to biological synapses.
Journal ArticleDOI
Deep-reinforcement-learning-based images segmentation for quantitative analysis of gold immunochromatographic strip
Nianyin Zeng,Han Li,Zidong Wang,Zidong Wang,Weibo Liu,Liu Songming,Fuad E. Alsaadi,Xiaohui Liu +7 more
TL;DR: A novel image segmentation method is developed in this paper for quantitative analysis of GICS based on the deep reinforcement learning (DRL), which can accurately distinguish the test line and the control line in the GICS images.
Proceedings ArticleDOI
Neural offset min-sum decoding
Loren Lugosch,Warren J. Gross +1 more
TL;DR: In this article, offset min-sum decoding is augmented with learnable offset parameters to speed up training and provide a feasible path to hardware architectures, which achieves error-correction performance within 0.1 dB of the multiplicative approach and as much as 1 dB better than traditional belief propagation.
Journal ArticleDOI
Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality.
TL;DR: Deep neural networks can predict in-hospital mortality based on automatically extractable intraoperative data, but are not (yet) superior to existing methods.
Posted Content
Invisible Backdoor Attacks on Deep Neural Networks via Steganography and Regularization
TL;DR: It is argued that the proposed invisible backdoor attacks can effectively thwart the state-of-the-art trojan backdoor detection approaches.
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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