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
BadNets: Evaluating Backdooring Attacks on Deep Neural Networks
TL;DR: It is shown that the outsourced training introduces new security risks: an adversary can create a maliciously trained network (a backdoored neural network, or a BadNet) that has the state-of-the-art performance on the user's training and validation samples but behaves badly on specific attacker-chosen inputs.
Journal ArticleDOI
Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey
TL;DR: A comprehensive survey of the applications of DL algorithms for different network layers, including physical layer modulation/coding, data link layer access control/resource allocation, and routing layer path search, and traffic balancing is performed.
Journal ArticleDOI
Multiagent cooperation and competition with deep reinforcement learning
Ardi Tampuu,Tambet Matiisen,Dorian Kodelja,Ilya Kuzovkin,Kristjan Korjus,Juhan Aru,Jaan Aru,Raul Vicente +7 more
TL;DR: The present work shows that Deep Q-Networks can become a useful tool for studying decentralized learning of multiagent systems coping with high-dimensional environments and describes the progression from competitive to collaborative behavior when the incentive to cooperate is increased.
Posted Content
Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon
TL;DR: A main point of the paper is seeing generic optimization problems as data points and inquiring what is the relevant distribution of problems to use for learning on a given task.
Journal ArticleDOI
Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study.
Jakob Nikolas Kather,Johannes Krisam,Pornpimol Charoentong,Pornpimol Charoentong,Tom Luedde,Esther Herpel,Cleo Aron Weis,Timo Gaiser,Alexander Marx,Nektarios A. Valous,Nektarios A. Valous,Dyke Ferber,Dyke Ferber,Lina Jansen,Constantino Carlos Reyes-Aldasoro,Inka Zörnig,Inka Zörnig,Dirk Jäger,Dirk Jäger,Hermann Brenner,Jenny Chang-Claude,Michael Hoffmeister,Niels Halama +22 more
TL;DR: It is shown that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images and was an independent prognostic factor for overall survival in a multivariable Cox proportional hazard model.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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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
TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.