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==1990 ...==
* [[Mathematician#SHochreiter|Sepp Hochreiter]] ('''1991'''). ''Untersuchungen zu dynamischen neuronalen Netzen''. Diploma thesis, [[Technical University of Munich|TU Munich]], advisor [[Jürgen Schmidhuber]], [http://people.idsia.ch/~juergen/SeppHochreiter1991ThesisAdvisorSchmidhuber.pdf pdf] (German) <ref>[http://people.idsia.ch/~juergen/fundamentaldeeplearningproblem.html Sepp Hochreiter's Fundamental Deep Learning Problem (1991)] by [[Jürgen Schmidhuber]], 2013</ref>
* [[Simon Lucas]] ('''1991'''). ''[https://eprints.soton.ac.uk/256263/ Connectionist architectures for syntactic pattern recognition]''. Ph.D. thesis, [https://en.wikipedia.org/wiki/University_of_Southampton University of Southampton]
* [[Mathematician#SHochreiter|Sepp Hochreiter]], [[Jürgen Schmidhuber]] ('''1997'''). ''Long short-term memory''. [https://en.wikipedia.org/wiki/Neural_Computation_%28journal%29 Neural Computation], Vol. 9, No. 8, [http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf pdf] <ref>[https://en.wikipedia.org/wiki/Long_short_term_memory Long short term memory from Wikipedia]</ref>
==2000 ...==
* [https://github.com/andravin Andrew Lavin], [https://github.com/scott-gray Scott Gray] ('''2015'''). ''Fast Algorithms for Convolutional Neural Networks''. [https://arxiv.org/abs/1509.09308 arXiv:1509.09308] <ref>[http://www.talkchess.com/forum/viewtopic.php?t=66025&start=3 Re: To TPU or not to TPU...] by [[Rémi Coulom]], [[CCC]], December 16, 2017</ref>
* [[Volodymyr Mnih]], [[Koray Kavukcuoglu]], [[David Silver]], [[Mathematician#AARusu|Andrei A. 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]] ('''2015'''). ''[http://www.nature.com/nature/journal/v518/n7540/abs/nature14236.html Human-level control through deep reinforcement learning]''. [https://en.wikipedia.org/wiki/Nature_%28journal%29 Nature], Vol. 518
* [https://scholar.google.ca/citations?user=yVtSOt8AAAAJ&hl=en Emmanuel Bengio], [https://scholar.google.ca/citations?user=9H77FYYAAAAJ&hl=en Pierre-Luc Bacon], [[Joelle Pineau]], [[Doina Precup]] ('''2015'''). ''Conditional Computation in Neural Networks for faster models''. [https://arxiv.org/abs/1511.06297 arXiv:1511.06297]
* [[Ilya Loshchilov]], [[Frank Hutter]] ('''2015'''). ''Online Batch Selection for Faster Training of Neural Networks''. [https://arxiv.org/abs/1511.06343 arXiv:1511.06343]
* [[Yuandong Tian]], [[Yan Zhu]] ('''2015'''). ''Better Computer Go Player with Neural Network and Long-term Prediction''. [http://arxiv.org/abs/1511.06410 arXiv:1511.06410], [http://nuit-blanche.blogspot.de/2016/02/iclr-2016-list-of-accepted-papers.html ICLR 2016] <ref>[http://www.technologyreview.com/view/544181/how-facebooks-ai-researchers-built-a-game-changing-go-engine/?utm_campaign=socialsync&utm_medium=social-post&utm_source=facebook How Facebook’s AI Researchers Built a Game-Changing Go Engine | MIT Technology Review], December 04, 2015</ref> <ref>[http://www.talkchess.com/forum/viewtopic.php?t=58514 Combining Neural Networks and Search techniques (GO)] by Michael Babigian, [[CCC]], December 08, 2015</ref> » [[Go]]
* [[Jürgen Schmidhuber]] ('''2015'''). ''[http://people.idsia.ch/~juergen/deep-learning-overview.html Deep Learning in Neural Networks: An Overview]''. [https://en.wikipedia.org/wiki/Neural_Networks_(journal) Neural Networks], Vol. 61
* [https://scholar.google.fr/citations?user=MN9Kfg8AAAAJ&hl=en Zachary C. Lipton], [https://www.linkedin.com/in/john-berkowitz-92b24a7b John Berkowitz], [[Charles Elkan]] ('''2015'''). ''A Critical Review of Recurrent Neural Networks for Sequence Learning''. [https://arxiv.org/abs/1506.00019 arXiv:1506.00019v4]
* [[Barak Oshri]], [[Nishith Khandwala]] ('''2015'''). ''Predicting Moves in Chess using Convolutional Neural Networks''. [http://cs231nvision.stanford.edu/teaching/cs231n/reports/2015/pdfs/ConvChess.pdf pdf] <ref>[https://github.com/BarakOshri/ConvChess GitHub - BarakOshri/ConvChess: Predicting Moves in Chess Using Convolutional Neural Networks]</ref> <ref>[http://www.talkchess.com/forum/viewtopic.php?t=63458 ConvChess CNN] by [[Brian Richardson]], [[CCC]], March 15, 2017</ref>
* [[Mathematician#YLeCun|Yann LeCun]], [[Mathematician#YBengio|Yoshua Bengio]], [[Mathematician#GEHinton|Geoffrey E. Hinton]] ('''2015'''). ''[http://www.nature.com/nature/journal/v521/n7553/full/nature14539.html Deep Learning]''. [https://en.wikipedia.org/wiki/Nature_%28journal%29 Nature], Vol. 521 <ref>[[Jürgen Schmidhuber]] ('''2015''') ''[http://people.idsia.ch/~juergen/deep-learning-conspiracy.html Critique of Paper by "Deep Learning Conspiracy" (Nature 521 p 436)]''.</ref>
* [[Peter H. Jin]], [[Kurt Keutzer]] ('''2015'''). ''Convolutional Monte Carlo Rollouts in Go''. [http://arxiv.org/abs/1512.03375 arXiv:1512.03375] » [[Go]], [[Monte-Carlo Tree Search|MCTS]]
* [[Tristan Cazenave]] ('''2017'''). ''[http://ieeexplore.ieee.org/document/7875402/ Residual Networks for Computer Go]''. [[IEEE#TOCIAIGAMES|IEEE Transactions on Computational Intelligence and AI in Games]], Vol. PP, No. 99, [http://www.lamsade.dauphine.fr/~cazenave/papers/resnet.pdf pdf]
* [[Shi-Jim Yen]], [[Ching-Nung Lin]], [[Guan-Lun Cheng]], [[Jr-Chang Chen]] ('''2017'''). ''[http://ieeexplore.ieee.org/document/7966187/ Deep Learning and Block Go]''. [http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7958416 IJCNN 2017]
* [[Matej Moravčík]], [[Mathematician#MSchmid|Martin Schmid]], [[Neil Burch]], [[Viliam Lisý]], [[Dustin Morrill]], [[Nolan Bard]], [[Trevor Davis]], [[Kevin Waugh]], [[Michael Johanson]], [[Michael Bowling]] ('''2017'''). ''[http://science.sciencemag.org/content/356/6337/508 DeepStack: Expert-level artificial intelligence in heads-up no-limit poker]''. [https://en.wikipedia.org/wiki/Science_(journal) Science], Vol. 356, No. 6337
* [[Tristan Cazenave]] ('''2017'''). ''Improved Policy Networks for Computer Go''. [[Advances in Computer Games 15]], [http://www.lamsade.dauphine.fr/~cazenave/papers/gofairsbn.pdf pdf]
* [[Hirotaka Kameko]], [[Jun Suzuki]], [[Naoki Mizukami]], [[Yoshimasa Tsuruoka]] ('''2017'''). ''Deep Reinforcement Learning with Hidden Layers on Future States''. [[Conferences#IJCAI2017|CGW@IJCAI 2017]], [http://www.lamsade.dauphine.fr/~cazenave/cgw2017/Kameko.pdf pdf]
* [[Keigo Kawamura]], [[Naoki Mizukami]], [[Yoshimasa Tsuruoka]] ('''2017'''). ''Neural Fictitious Self-Play in Imperfect Information Games with Many Players''. [[Conferences#IJCAI2017|CGW@IJCAI 2017]], [http://www.lamsade.dauphine.fr/~cazenave/cgw2017/Kawamura.pdf pdf]
* [[Thomas Philip Runarsson]] ('''2017'''). ''[https://link.springer.com/chapter/10.1007/978-3-319-75931-9_3 Deep Preference Neural Network for Move Prediction in Board Games]''. [[Conferences#IJCAI2017|CGW@IJCAI 2017]]
* [[David Silver]], [[Julian Schrittwieser]], [[Karen Simonyan]], [[Ioannis Antonoglou]], [[Shih-Chieh Huang|Aja Huang]], [[Arthur Guez]], [[Thomas Hubert]], [[Lucas Baker]], [[Matthew Lai]], [[Adrian Bolton]], [[Yutian Chen]], [[Timothy Lillicrap]], [[Fan Hui]], [[Laurent Sifre]], [[George van den Driessche]], [[Thore Graepel]], [[Demis Hassabis]] ('''2017'''). ''[https://www.nature.com/nature/journal/v550/n7676/full/nature24270.html Mastering the game of Go without human knowledge]''. [https://en.wikipedia.org/wiki/Nature_%28journal%29 Nature], Vol. 550, [https://www.gwern.net/docs/rl/2017-silver.pdf pdf] <ref>[https://deepmind.com/blog/alphago-zero-learning-scratch/ AlphaGo Zero: Learning from scratch] by [[Demis Hassabis]] and [[David Silver]], [[DeepMind]], October 18, 2017</ref>
* [[Shantanu Thakoor]], [[Surag Nair]], [[Megha Jhunjhunwala]] ('''2017'''). ''Learning to Play Othello Without Human Knowledge''. [[Stanford University]], [https://github.com/suragnair/alpha-zero-general/blob/master/pretrained_models/writeup.pdf pdf] » [[AlphaZero]], [[Monte-Carlo Tree Search|MCTS]], [[Othello]] <ref>[https://github.com/suragnair/alpha-zero-general GitHub - suragnair/alpha-zero-general: A clean and simple implementation of a self-play learning algorithm based on AlphaGo Zero (any game, any framework!)]</ref>
* [[Masatoshi Hidaka]], [https://dblp.org/pers/hd/k/Kikura:Yuichiro Yuichiro Kikura], [https://dblp.org/pers/hd/u/Ushiku:Yoshitaka Yoshitaka Ushiku], [https://dblp.org/pers/hd/h/Harada:Tatsuya Tatsuya Harada] ('''2017'''). ''WebDNN: Fastest DNN Execution Framework on Web Browser''. [https://dblp.org/db/conf/mm/mm2017.html ACM Multimedia 2017], [https://www.mi.t.u-tokyo.ac.jp/assets/publication/webdnn.pdf pdf] <ref>[https://github.com/mil-tokyo/webdnn GitHub - mil-tokyo/webdnn: The Fastest DNN Running Framework on Web Browser]</ref>
* [https://www.researchgate.net/profile/Francisco_Matos3 Francisco A. Matos], [[Diogo R. Ferreira]], [https://www.researchgate.net/profile/P_Carvalho2 Pedro J. Carvalho], [https://en.wikipedia.org/wiki/Joint_European_Torus JET] Contributors ('''2017'''). ''Deep learning for plasma tomography using the bolometer system at JET''. [https://arxiv.org/abs/1701.00322 arXiv:1701.00322]
* [[Masatoshi Hidaka]], [https://dblp.org/pers/hd/m/Miura:Ken Ken Miura], [https://dblp.org/pers/hd/h/Harada:Tatsuya Tatsuya Harada] ('''2017'''). ''Development of JavaScript-based deep learning platform and application to distributed training''. [https://arxiv.org/abs/1702.01846 arXiv:1702.01846], [https://dblp.org/db/conf/iclr/iclr2017w.html ICLR 2017]
* [[Mathematician#SIoffe|Sergey Ioffe]] ('''2017'''). ''Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models''. [https://arxiv.org/abs/1702.03275 arXiv:1702.03275]
* [[Risto Miikkulainen]], et al. ('''2017'''). ''Evolving Deep Neural Networks''. [https://arxiv.org/abs/1703.00548 arXiv:1703.00548]
* [[Thomas Anthony]], [[Zheng Tian]], [[David Barber]] ('''2017'''). ''Thinking Fast and Slow with Deep Learning and Tree Search''. [https://arxiv.org/abs/1705.08439 arXiv:1705.08439]
* [[Ti-Rong Wu]], [[I-Chen Wu]], [[Guan-Wun Chen]], [[Ting-Han Wei]], [[Tung-Yi Lai]], [[Hung-Chun Wu]], [[Li-Cheng Lan]] ('''2017'''). ''Multi-Labelled Value Networks for Computer Go''. [https://arxiv.org/abs/1705.10701 arXiv:1705.10701]
* [[Olivier Bousquet]], [[Sylvain Gelly]], [[Karol Kurach]], [[Marc Schoenauer]], [[Michèle Sebag]], [[Olivier Teytaud]], [[Damien Vincent]] ('''2017'''). ''Toward Optimal Run Racing: Application to Deep Learning Calibration''. [https://arxiv.org/abs/1706.03199 arXiv:1706.03199]
* [[Adams Wei Yu]], [[Lei Huang]], [[Qihang Lin]], [[Mathematician#RRSalakhutdinov|Ruslan Salakhutdinov]], [[Jaime Carbonell]] ('''2017'''). ''Block-Normalized Gradient Method: An Empirical Study for Training Deep Neural Network''. [https://arxiv.org/abs/1707.04822 arXiv:1707.04822]
* [[Alice Schoenauer-Sebag]], [[Marc Schoenauer]], [[Michèle Sebag]] ('''2017'''). ''Stochastic Gradient Descent: Going As Fast As Possible But Not Faster''. [https://arxiv.org/abs/1709.01427 arXiv:1709.01427]
* [http://www.peterhenderson.co/ Peter Henderson], [https://scholar.google.ca/citations?user=2_4Rs44AAAAJ&hl=en Riashat Islam], [[Philip Bachman]], [[Joelle Pineau]], [[Doina Precup]], [https://scholar.google.ca/citations?user=gFwEytkAAAAJ&hl=en David Meger] ('''2017'''). ''Deep Reinforcement Learning that Matters''. [https://arxiv.org/abs/1709.06560 arXiv:1709.06560]
* [[Matthia Sabatelli]] ('''2017'''). ''Learning to Play Chess with Minimal Lookahead and Deep Value Neural Networks''. Master's thesis, [https://en.wikipedia.org/wiki/University_of_Groningen University of Groningen], [https://www.ai.rug.nl/~mwiering/Thesis_Matthia_Sabatelli.pdf pdf] <ref>[https://github.com/paintception/DeepChess GitHub - paintception/DeepChess]</ref>
* [[Marc Lanctot]], [[Vinícius Flores Zambaldi]], [[Audrunas Gruslys]], [[Angeliki Lazaridou]], [[Karl Tuyls]], [[Julien Pérolat]], [[David Silver]], [[Thore Graepel]] ('''2017'''). ''A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning''. [https://arxiv.org/abs/1711.00832 arXiv:1711.00832]
* [https://scholar.google.com/citations?user=tiE4g64AAAAJ&hl=en Maithra Raghu], [https://scholar.google.com/citations?user=ZZNxNAYAAAAJ&hl=en Alex Irpan], [[Mathematician#JAndreas|Jacob Andreas]], [[Mathematician#RKleinberg|Robert Kleinberg]], [[Quoc V. Le]], [[Jon Kleinberg]] ('''2017'''). ''Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?'' [https://arxiv.org/abs/1711.02301 arXiv:1711.02301]
* [[Paweł Liskowski]], [[Wojciech Jaśkowski]], [[Krzysztof Krawiec]] ('''2017'''). ''Learning to Play Othello with Deep Neural Networks''. [https://arxiv.org/abs/1711.06583 arXiv:1711.06583] <ref>[https://en.wikipedia.org/wiki/Edax_(computing) Edax] by [[Richard Delorme]]</ref>
* [[David Silver]], [[Thomas Hubert]], [[Julian Schrittwieser]], [[Ioannis Antonoglou]], [[Matthew Lai]], [[Arthur Guez]], [[Marc Lanctot]], [[Laurent Sifre]], [[Dharshan Kumaran]], [[Thore Graepel]], [[Timothy Lillicrap]], [[Karen Simonyan]], [[Demis Hassabis]] ('''2017'''). ''Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm''. [https://arxiv.org/abs/1712.01815 arXiv:1712.01815] » [[AlphaZero]]
* [[George Philipp]], [[Jaime Carbonell]] ('''2017'''). ''Nonparametric Neural Networks''. [https://arxiv.org/abs/1712.05440 arXiv:1712.05440]
* [[George Philipp]], [[Mathematician#DawnSong|Dawn Song]], [[Jaime Carbonell]] ('''2017'''). ''The exploding gradient problem demystified - definition, prevalence, impact, origin, tradeoffs, and solutions''. [https://arxiv.org/abs/1712.05577 arXiv:1712.05577]
* [https://dblp.uni-trier.de/pers/hd/c/Chen:Qiming Qiming Chen], [[Ren Wu]] ('''2017'''). ''CNN Is All You Need''. [https://arxiv.org/abs/1712.09662 arXiv:1712.09662]
'''2018'''
* [[Paweł Liskowski]], [[Wojciech Jaśkowski]], [[Krzysztof Krawiec]] ('''2018'''). ''Learning to Play Othello with Deep Neural Networks''. [[IEEE#TOG|IEEE Transactions on Games]]
* [[David J. Wu]] ('''2019'''). ''Accelerating Self-Play Learning in Go''. [https://arxiv.org/abs/1902.10565 arXiv:1902.10565]
* [[Aston Zhang]], [[Zack C. Lipton]], [[Mu Li]], [[Alex J. Smola]] ('''2019'''). ''[https://www.d2l.ai/index.html Dive into Deep Learning]''. An interactive deep learning book with code, math, and discussions
* [[Johannes Czech]] ('''2019'''). ''Deep Reinforcement Learning for Crazyhouse''. Master thesis, [[Darmstadt University of Technology|TU Darmstadt]], [https://ml-research.github.io/papers/czech2019deep.pdf pdf] » [[CrazyAra]]
* [[Hsiao-Chung Hsieh]], [[Ti-Rong Wu]], [[Ting-Han Wei]], [[I-Chen Wu]] ('''2019'''). ''Net2Net Extension for the AlphaGo Zero Algorithm''. [[Advances in Computer Games 16]]
* [[Tomihiro Kimura]], [[Kokolo Ikeda]] ('''2019'''). ''Designing Policy Network with Deep Learning in Turn-Based Strategy Games''. [[Advances in Computer Games 16]]
==2020 ...==
* [[Garrett Bingham]], [[William Macke]], [[Risto Miikkulainen]] ('''2020'''). ''Evolutionary Optimization of Deep Learning Activation Functions''. [https://arxiv.org/abs/2002.07224 arXiv:2002.07224]
* [[Jason Liang]], [[Santiago Gonzalez]], [[Risto Miikkulainen]] ('''2020'''). ''Population-Based Training for Loss Function Optimization''. [https://arxiv.org/abs/2002.04225 arXiv:2002.04225]
* [[Julian Schrittwieser]], [[Ioannis Antonoglou]], [[Thomas Hubert]], [[Karen Simonyan]], [[Laurent Sifre]], [[Simon Schmitt]], [[Arthur Guez]], [[Edward Lockhart]], [[Demis Hassabis]], [[Thore Graepel]], [[Timothy Lillicrap]], [[David Silver]] ('''2020'''). ''[https://www.nature.com/articles/s41586-020-03051-4 Mastering Atari, Go, chess and shogi by planning with a learned model]''. [https://en.wikipedia.org/wiki/Nature_%28journal%29 Nature], Vol. 588 <ref>[https://deepmind.com/blog/article/muzero-mastering-go-chess-shogi-and-atari-without-rules?fbclid=IwAR3mSwrn1YXDKr9uuGm2GlFKh76wBilex7f8QvBiQecwiVmAvD6Bkyjx-rE MuZero: Mastering Go, chess, shogi and Atari without rules]</ref> <ref>[https://github.com/koulanurag/muzero-pytorch GitHub - koulanurag/muzero-pytorch: Pytorch Implementation of MuZero]</ref>
* [[Reid McIlroy-Young]], [[Siddhartha Sen]], [[Jon Kleinberg]], [[Ashton Anderson]] ('''2020'''). ''Aligning Superhuman AI with Human Behavior: Chess as a Model System''. In Proceedings of the 26th [[ACM#SIGKDD|ACM SIGKDD 2020]], [https://arxiv.org/abs/2006.01855 arXiv:2006.01855] » [[Maia Chess]]
* [[Reid McIlroy-Young]], [[Russell Wang]], [[Siddhartha Sen]], [[Jon Kleinberg]], [[Ashton Anderson]] ('''2020'''). ''Learning Personalized Models of Human Behavior in Chess''. [https://arxiv.org/abs/2008.10086 arXiv:2008.10086]
* [[Tristan Cazenave]], [[Yen-Chi Chen]], [[Guan-Wei Chen]], [[Shi-Yu Chen]], [[Xian-Dong Chiu]], [[Julien Dehos]], [[Maria Elsa]], [[Qucheng Gong]], [[Hengyuan Hu]], [[Vasil Khalidov]], [[Cheng-Ling Li]], [[Hsin-I Lin]], [[Yu-Jin Lin]], [[Xavier Martinet]], [[Vegard Mella]], [[Jeremy Rapin]], [[Baptiste Roziere]], [[Gabriel Synnaeve]], [[Fabien Teytaud]], [[Olivier Teytaud]], [[Shi-Cheng Ye]], [[Yi-Jun Ye]], [[Shi-Jim Yen]], [[Sergey Zagoruyko]] ('''2020'''). ''Polygames: Improved zero learning''. [[ICGA Journal#42_4|ICGA Journal, Vol. 42, No. 4]], [https://arxiv.org/abs/2001.09832 arXiv:2001.09832]
* [https://scholar.google.com/citations?user=BJwJ0gYAAAAJ&hl=en Caspar van Leeuwen], [https://scholar.google.com/citations?user=qAk3LVgAAAAJ&hl=en Damian Podareanu], [[Valeriu Codreanu]], [https://github.com/maxwelltsai Maxwell X. Cai], [https://github.com/axeber01 Axel Berg], [[Simon Portegies Zwart]], [https://dblp.org/pid/262/3588.html Robin Stoffer], [https://dblp.org/pid/262/3560.html Menno Veerman], [https://scholar.google.com/citations?user=vjf3-RgAAAAJ&hl=en Chiel van Heerwaarden], [https://scholar.google.com/citations?user=kjtZbDMAAAAJ&hl=en Sydney Otten], [https://scholar.google.com/citations?user=yfuZDxsAAAAJ&hl=en Sascha Caron], [https://scholar.google.com/citations?user=21G0R_AAAAAJ&hl=en Cunliang Geng], [https://scholar.google.com/citations?user=9VL05xkAAAAJ&hl=en Francesco Ambrosetti], [https://scholar.google.com/citations?user=GLIgELEAAAAJ&hl=en Alexandre M.J.J. Bonvin] ('''2020'''). ''Deep-learning enhancement of large scale numerical simulations''. [https://arxiv.org/abs/2004.03454 arXiv:2004.03454]
* [[Johannes Czech]], [[Moritz Willig]], [[Alena Beyer]], [[Kristian Kersting]], [[Johannes Fürnkranz]] ('''2020'''). ''[https://www.frontiersin.org/articles/10.3389/frai.2020.00024/full Learning to Play the Chess Variant Crazyhouse Above World Champion Level With Deep Neural Networks and Human Data]''. [https://www.frontiersin.org/journals/artificial-intelligence# Frontiers in Artificial Intelligence] » [[CrazyAra]]
* [[Quentin Cohen-Solal]] ('''2020'''). ''Learning to Play Two-Player Perfect-Information Games without Knowledge''. [https://arxiv.org/abs/2008.01188 arXiv:2008.01188]
* [[Quentin Cohen-Solal]], [[Tristan Cazenave]] ('''2020'''). ''Minimax Strikes Back''. [https://arxiv.org/abs/2012.10700 arXiv:2012.10700]
'''2021'''
* [[Johannes Czech]], [[Patrick Korus]], [[Kristian Kersting]] ('''2021'''). ''[https://ojs.aaai.org/index.php/ICAPS/article/view/15952 Improving AlphaZero Using Monte-Carlo Graph Search]''. [https://ojs.aaai.org/index.php/ICAPS/issue/view/380 Proceedings of the Thirty-First International Conference on Automated Planning and Scheduling], Vol. 31, [https://www.ml.informatik.tu-darmstadt.de/papers/czech2021icaps_mcgs.pdf pdf]
* [[Maximilian Langer]] ('''2021'''). ''Evaluation of Monte-Carlo Tree Search for Xiangqi''. B.Sc. thesis, advisors [[Kristian Kersting]] and [[Johannes Czech]], [[Darmstadt University of Technology|TU Darmstadt]], [https://ml-research.github.io/papers/langer2021xiangqi.pdf pdf] » [[Chinese Chess|Xiangqi]]
* [[Maximilian Alexander Gehrke]] ('''2021'''). ''Assessing Popular Chess Variants Using Deep Reinforcement Learning''. Master thesis, [[Darmstadt University of Technology|TU Darmstadt]], [https://ml-research.github.io/papers/gehrke2021assessing.pdf pdf] » [[CrazyAra]]
* [[Dominik Klein]] ('''2021'''). ''[https://github.com/asdfjkl/neural_network_chess Neural Networks For Chess]''. [https://github.com/asdfjkl/neural_network_chess/releases/tag/v1.1 Release Version 1.1 · GitHub] <ref>[https://www.talkchess.com/forum3/viewtopic.php?f=2&t=78283 Book about Neural Networks for Chess] by dkl, [[CCC]], September 29, 2021</ref>
* [[Thomas McGrath]], [[Andrei Kapishnikov]], [[Nenad Tomašev]], [[Adam Pearce]], [[Demis Hassabis]], [[Been Kim]], [[Ulrich Paquet]], [[Vladimir Kramnik]] ('''2021'''). ''Acquisition of Chess Knowledge in AlphaZero''. [https://arxiv.org/abs/2111.09259 arXiv:2111.09259] <ref>[https://en.chessbase.com/post/acquisition-of-chess-knowledge-in-alphazero Acquisition of Chess Knowledge in AlphaZero], [[ChessBase|ChessBase News]], November 18, 2021</ref>
* [[Tristan Cazenave]], [[Julien Sentuc]], [[Mathurin Videau]] ('''2021'''). ''Cosine Annealing, Mixnet and Swish Activation for Computer Go''. [[Advances in Computer Games 17]]
* [[Hung-Jui Chang]], [[Cheng Yueh]], [[Gang-Yu Fan]], [[Ting-Yu Lin]], [[Tsan-sheng Hsu]] ('''2021'''). ''Opponent Model Selection Using Deep Learning''. [[Advances in Computer Games 17]]
* [[Rejwana Haque]], [[Ting Han Wei]], [[Martin Müller]] ('''2021'''). ''On the Road to Perfection? Evaluating Leela Chess Zero Against Endgame Tablebases''. [[Advances in Computer Games 17]]
* [[Boris Doux]], [[Benjamin Negrevergne]], [[Tristan Cazenave]] ('''2021'''). ''Deep Reinforcement Learning for Morpion Solitaire''. [[Advances in Computer Games 17]]
* [[Aðalsteinn Pálsson]], [[Yngvi Björnsson]] ('''2021'''). ''Evaluating Interpretability Methods for DNNs in Game-Playing Agents''. [[Advances in Computer Games 17]]
* [[Dennis Soemers]], [[Vegard Mella]], [[Cameron Browne]], [[Olivier Teytaud]] ('''2021'''). ''Deep learning for general game playing with Ludii and Polygames''. [[ICGA Journal#43_3|ICGA Journal, Vol. 43, No. 3]]
=Forum Posts=
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=71301 A question to MCTS + NN experts] by [[Maksim Korzh]], [[CCC]], July 17, 2019 » [[Monte-Carlo Tree Search]]
: [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=71301&start=3 Re: A question to MCTS + NN experts] by [[Daniel Shawul]], [[CCC]], July 17, 2019
* [https://www.game-ai-forum.org/viewtopic.php?f=21&t=694 My home-made CUDA kernel for convolutions] by [[Rémi Coulom]], [[Computer Chess Forums|Game-AI Forum]], November 09, 2019
* [https://groups.google.com/d/msg/fishcooking/wOfRuzTSi_8/VgjN8MmSBQAJ high dimensional optimization] by [[Warren D. Smith]], [[Computer Chess Forums|FishCooking]], December 27, 2019 <ref>[[Mathematician#YDauphin|Yann Dauphin]], [[Mathematician#RPascanu|Razvan Pascanu]], [[Mathematician#CGulcehre|Caglar Gulcehre]], [[Mathematician#KCho|Kyunghyun Cho]], [[Mathematician#SGanguli|Surya Ganguli]], [[Mathematician#YBengio|Yoshua Bengio]] ('''2014'''). ''Identifying and attacking the saddle point problem in high-dimensional non-convex optimization''. [https://arxiv.org/abs/1406.2572 arXiv:1406.2572]</ref>
==2020 ...==
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74077 How to work with batch size in neural network] by Gertjan Brouwer, [[CCC]], June 02, 2020
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=75985 Maiachess] by [[Marc-Philippe Huget]], [[CCC]], December 04, 2020 » [[Maia Chess]]
=External Links=
* [https://github.com/fchollet/keras/tree/master/keras keras/keras at master · fchollet/keras · GitHub] » [[Python]]
* [https://keras.io/ Keras Documentation]
* [https://en.wikipedia.org/wiki/PyTorch PyTorch from Wikipedia] » [[Python]]
* [https://en.wikipedia.org/wiki/TensorFlow TensorFlow from Wikipedia]
* [https://github.com/jtoy/awesome-tensorflow GitHub - jtoy/awesome-tensorflow: TensorFlow]
* [https://en.wikipedia.org/wiki/Scikit-learn scikit-learn from Wikipedia] » [[Python]]
* [https://en.wikipedia.org/wiki/Theano_(software) Theano (software) from Wikipedia]
===Chess===
* [https://github.com/benediamond/leela-chess GitHub - benediamond/leela-chess: A chess adaption of GCP's Leela Zero]
* [https://github.com/glinscott/leela-chess GitHub - glinscott/leela-chess: A chess adaption of GCP's Leela Zero] » [[Leela Chess Zero]]
* [https://github.com/CSSLab/maia-chess GitHub - CSSLab/maia-chess: Human like chess engines] » [[Maia Chess]]
* [https://github.com/Zeta36/chess-alpha-zero GitHub - Zeta36/chess-alpha-zero: Chess reinforcement learning by AlphaGo Zero methods] » [[Zeta36]] <ref>[http://www.talkchess.com/forum/viewtopic.php?t=65909&start=41 Re: Google's AlphaGo team has been working on chess] by [[Brian Richardson]], [[CCC]], December 09, 2017</ref>
===Games===
* [https://github.com/gcp/leela-zero GitHub - gcp/leela-zero: Go engine with no human-provided knowledge, modeled after the AlphaGo Zero paper] by [[Gian-Carlo Pascutto]] et al. » [[Leela Zero]]
* [https://medium.com/applied-data-science/how-to-build-your-own-alphazero-ai-using-python-and-keras-7f664945c188 How to build your own AlphaZero AI using Python and Keras] by [https://www.linkedin.com/in/davidtfoster/ David Foster], January 26, 2018 » [[AlphaZero]], [[Connect Four]], [[Python]] <ref>[http://www.talkchess.com/forum/viewtopic.php?t=66443 Connect 4 AlphaZero implemented using Python...] by [[Steve Maughan]], [[CCC]], January 29, 2018</ref>
* [https://ai.facebook.com/blog/open-sourcing-polygames-a-new-framework-for-training-ai-bots-through-self-play/ Open-sourcing Polygames, a new framework for training AI bots through self-play]
* [https://github.com/facebookarchive/Polygames GitHub - facebookarchive/Polygames: The project is a platform of zero learning with a library of games]
===Music Generation===
* [http://www.asimovinstitute.org/analyzing-deep-learning-tools-music/ Analyzing Six Deep Learning Tools for Music Generation] by [http://www.asimovinstitute.org/team/ Frank Brinkkemper], [http://www.asimovinstitute.org/ The Asimov Institute], October 5, 2016
* [http://web.stanford.edu/~surag/posts/alphazero.html A Simple Alpha(Go) Zero Tutorial] by [[Surag Nair]], [[Stanford University]], December 29, 2017 » [[AlphaZero]], [[Monte-Carlo Tree Search|MCTS]] <ref>[http://www.talkchess.com/forum/viewtopic.php?t=66179 A Simple Alpha(Go) Zero Tutorial] by Oliver Roese, [[CCC]], December 30, 2017</ref>
: [https://github.com/suragnair/alpha-zero-general GitHub - suragnair/alpha-zero-general: A clean and simple implementation of a self-play learning algorithm based on AlphaGo Zero (any game, any framework!)]
* [https://software.intel.com/content/www/us/en/develop/articles/lower-numerical-precision-deep-learning-inference-and-training.html Lower Numerical Precision Deep Learning Inference and Training] by [https://community.intel.com/t5/user/viewprofilepage/user-id/134067 Andres Rodriguez] et al., [[Intel]], January 19, 2018 » [[AVX-512]]
==Videos==
* [https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ Deep Learning SIMPLIFIED: The Series Intro] [https://en.wikipedia.org/wiki/YouTube YouTube] Videos
* <span id="SchmidhuberVideoDeepLearning"></span>[https://www.youtube.com/watch?v=6bOMf9zr7N8 Deep Learning RNNaissance] with [[Jürgen Schmidhuber]], [https://en.wikipedia.org/wiki/YouTube YouTube] Video
: {{#evu:https://www.youtube.com/watch?v=6bOMf9zr7N8|alignment=left|valignment=top}}
