The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess
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“Each program was run on the hardware for which it was designed ( 23 ): Stockfish and Elmo used 44 central processing unit (CPU) cores (as in the TCEC world championship), whereas AlphaZero and AlphaGo Zero used a single machine with four first-generation TPUs and 44 CPU cores ( 24 ).”
From A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play (2018) · cites this paper as [24]
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Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841
An analysis of alpha-beta pruning
Knuth, D. E., & Moore, R. W. (1975) · Artificial Intelligence · doi:10.1016/0004-3702(75)90019-3 · Cited by 1,029
Schaeffer, J., BURCH, N. R., Björnsson, Y., Kishimoto, A., Müller, M., Lake, R., Lu, P., & Sutphen, S. (2007) · Science · doi:10.1126/science.1144079 · Cited by 452
Genetic Algorithms for Evolving Computer Chess Programs
David, O. E., van den Herik, H. J., Koppel, M., & Netanyahu, N. S. (2013) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2013.2285111 · Cited by 38
Self-citationExpert-driven genetic algorithms for simulating evaluation functions
David-Tabibi, O., Koppel, M., & Netanyahu, N. S. (2010) · Genetic Programming and Evolvable Machines · doi:10.1007/s10710-010-9103-4 · Cited by 11
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Research works printed but not linked: 9
- Baxter, J., Tridgell, A., Weaver, L.: Learning to play chess using temporal-differences. Mach. Learn. 40(3), 243–263 (2000) · DOI
- Elo, A.E.: The Rating of Chessplayers, Past and Present. Batsford, London (1978)
- Hinton, G., Vinyals, O., Dean, J.: Distilling knowledge in a neural network. In: Deep Learning and Representation Learning Workshop, NIPS (2014)
- Lai, M.: Giraffe: Using deep reinforcement learning to play chess. Master’s Thesis, Imperial College London (2015)
- Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS (2015)
- Romero, A., Ballas, N., Ebrahimi Kahou, S., Chassang, A., Gatta, C., Bengio, Y.: FitNets: hints for thin deep nets. In: ICLR (2015)
- Schaeffer, J., Hlynka, M., Jussila, V.: Temporal difference learning applied to a high-performance game-playing program. In: Joint Conference on Artificial Intelligence (2001)
- Tesauro, G.: Practical issues in temporal difference learning. Mach. Learn. 8(3–4), 257–277 (1992)
- Wiering, M.A.: TD learning of game evaluation functions with hierarchical neural architectures. Master’s Thesis, University of Amsterdam (1995)
Cited by
Sorted by citations
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al. (2018) · Science · doi:10.1126/science.aar6404 · Cited by 3,562
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al. (2017) · arXiv · doi:10.48550/arxiv.1712.01815 · Cited by 1,102
“The high performance of AlphaZero, using MCTS, calls into question the widely held belief (4, 11) that alpha-beta search is inherently superior in these domains.”
Background
Metadata
- Authors
- David, O. E., Netanyahu, N. S., & Wolf, L.
- Affiliations
- Bar-Ilan University (IL)
- Tel Aviv University (IL)
- Corresponding author
- David, Omid E.
- ORCID
- Wolf, Lior
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 88-96
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2509966261
- Cited by
- 76
- References
- 6
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 7 · 2023: 13 · 2022: 5 · 2021: 12 · 2020: 6 · 2019: 13 · 2018: 11 · 2017: 3
- FWCI
- 4.85
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- chess, end-to-end learning, deep neural network, unsupervised pre-training, supervised exercise
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