Handwritten digit recognition with a back-propagation network
Advances in neural information processing systems 2
Temporal difference learning and TD-Gammon
Communications of the ACM
Computer Go: an AI oriented survey
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
CG '00 Revised Papers from the Second International Conference on Computers and Games
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Bayesian pattern ranking for move prediction in the game of Go
ICML '06 Proceedings of the 23rd international conference on Machine learning
A Sparse and Locally Shift Invariant Feature Extractor Applied to Document Images
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
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Building a strong computer Go player is a longstanding open problem. In this paper we consider the related problem of predicting the moves made by Go experts in professional games. The ability to predict experts' moves is useful, because it can, in principle, be used to narrow the search done by a computer Go player. We applied an ensemble of convolutional neural networks to this problem. Our main result is that the ensemble learns to predict 36.9% of the moves made in test expert Go games, improving upon the state of the art, and that the best single convolutional neural network of the ensemble achieves 34% accuracy. This network has less than 104parameters.