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
Skin cancer detection: Applying a deep learning based model driven architecture in the cloud for classifying dermal cell images
TL;DR: A model-driven architecture in the cloud, that uses deep learning algorithms in its core implementations, is used to construct models that assist in predicting skin cancer with improved accuracy.
Journal ArticleDOI
Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection
TL;DR: This study develops an automatic classification system of brain images in magnetic resonance imaging (MRI) that is effective and feasible, and superior to 12 state-of-the-art approaches.
Journal ArticleDOI
Machine Learning With Neuromorphic Photonics
Thomas Ferreira de Lima,Hsuan-Tung Peng,Alexander N. Tait,Mitchell A. Nahmias,Heidi B. Miller,Bhavin J. Shastri,Paul R. Prucnal +6 more
TL;DR: This paper discusses the physical advantages of photonic processing systems, and the underlying device models that allow practical systems to be constructed, and describes several real-world applications for control and deep learning inference.
Proceedings ArticleDOI
SINT++: Robust Visual Tracking via Adversarial Positive Instance Generation
TL;DR: The positive samples generation network (PSGN) is introduced to sampling massive diverse training data through traversing over the constructed target object manifold and generated diverse target object images can enrich the training dataset and enhance the robustness of visual trackers.
Posted Content
Learning and Querying Fast Generative Models for Reinforcement Learning
Lars Buesing,Theophane Weber,Sébastien Racanière,S. M. Ali Eslami,Danilo Jimenez Rezende,David P. Reichert,Fabio Viola,Frederic Besse,Karol Gregor,Demis Hassabis,Daan Wierstra +10 more
TL;DR: It is demonstrated that agents which query these models for decision making outperform strong model-free baselines on the game MSPACMAN, demonstrating the potential of using learned environment models for planning.
References
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