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* [[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]
* [[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=
* [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}}
