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=ANNs=
[https://en.wikipedia.org/wiki/Artificial_neural_network Artificial Neural Networks] ('''ANNs''') are a family of [https://en.wikipedia.org/wiki/Machine_learning statistical learning] devices or algorithms used in [https://en.wikipedia.org/wiki/Regression_analysis regression], and [https://en.wikipedia.org/wiki/Binary_classification binary] or [[https://en.wikipedia.org/wiki/Multiclass_classification multiclass classification|multiclass classification]], implemented in [[Hardware|hardware]] or [[Software|software]] inspired by their biological counterparts. The [https://en.wikipedia.org/wiki/Artificial_neuron artificial neurons] of one or more layers receive one or more inputs (representing dendrites), and after being weighted, sum them to produce an output (representing a neuron's axon). The sum is passed through a [https://en.wikipedia.org/wiki/Nonlinear_system nonlinear] function known as an [https://en.wikipedia.org/wiki/Activation_function activation function] or transfer function. The transfer functions usually have a [https://en.wikipedia.org/wiki/Sigmoid_function sigmoid shape], but they may also take the form of other non-linear functions, [https://en.wikipedia.org/wiki/Piecewise piecewise] linear functions, or [https://en.wikipedia.org/wiki/Artificial_neuron#Step_function step functions] <ref>[https://en.wikipedia.org/wiki/Artificial_neuron Artificial neuron from Wikipedia]</ref>. The weights of the inputs of each layer are tuned to minimize a [https://en.wikipedia.org/wiki/Loss_function cost or loss function], which is a task in [https://en.wikipedia.org/wiki/Mathematical_optimization mathematical optimization] and machine learning.
==Perceptron==
In December 2017, the [[Google]] [[DeepMind]] team along with former [[Giraffe]] author [[Matthew Lai]] reported on their generalized [[AlphaZero]] algorithm, combining [[Deep Learning|Deep learning]] with [[Monte-Carlo Tree Search]]. AlphaZero can achieve, tabula rasa, superhuman performance in many challenging domains with some training effort. Starting from random play, and given no domain knowledge except the game rules, AlphaZero achieved a superhuman level of play in the games of chess and [[Shogi]] as well as Go, and convincingly defeated a world-champion program in each case <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]</ref>. The open souece projects [[Leela Zero]] (Go) and its chess adaptation [[Leela Chess Zero]] successfully re-implemented the ideas of DeepMind.
===NNUE===
[[NNUE]] reverse of ƎUИИ - Efficiently Updatable Neural Networks, is an NN architecture intended to replace the [[Evaluation|evaluation]] of [[Shogi]], [[Chess|chess]] and other board game playing [[Alpha-Beta|alpha-beta]] searchers. NNUE was introduced in 2018 by [[Yu Nasu]] <ref>[[Yu Nasu]] ('''2018'''). ''ƎUИИ Efficiently Updatable Neural-Network based Evaluation Functions for Computer Shogi''. Ziosoft Computer Shogi Club, [https://github.com/ynasu87/nnue/blob/master/docs/nnue.pdf pdf] (Japanese with English abstract)[https://github.com/asdfjkl/nnue GitHub - asdfjkl/nnue translation]</ref>,
and was used in Shogi adaptations of [[Stockfish]] such as [[YaneuraOu]] <ref>[https://github.com/yaneurao/YaneuraOu GitHub - yaneurao/YaneuraOu: YaneuraOu is the World's Strongest Shogi engine(AI player), WCSC29 1st winner, educational and USI compliant engine]</ref> ,
and [[Kristallweizen]] <ref>[https://github.com/Tama4649/Kristallweizen/ GitHub - Tama4649/Kristallweizen: 第29回世界コンピュータ将棋選手権 準優勝のKristallweizenです。]</ref>, apparently with [[AlphaZero]] strength <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=2&t=72754 The Stockfish of shogi] by [[Larry Kaufman]], [[CCC]], January 07, 2020</ref>. [[Hisayori Noda|Nodchip]] incorporated NNUE into the chess playing Stockfish 10 as a proof of concept <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=2&t=74059 Stockfish NN release (NNUE)] by [[Henk Drost]], [[CCC]], May 31, 2020</ref>, yielding in the hype about [[Stockfish NNUE]] in summer 2020 <ref>[http://yaneuraou.yaneu.com/2020/06/19/stockfish-nnue-the-complete-guide/ Stockfish NNUE – The Complete Guide], June 19, 2020 (Japanese and English)</ref>.
* [[NNUE]]
* [[Pattern Recognition]]
* [[David E. Moriarty#SANE|SANE]]
* [[Temporal Difference Learning]]
==1990 ...==
* [[Mathematician#PWerbos|Paul Werbos]] ('''1990'''). ''Backpropagation Through Time: What It Does and How to Do It''. Proceedings of the [[IEEE]], Vol. 78, No. 10, [http://deeplearning.cs.cmu.edu/pdfs/Werbos.backprop.pdf pdf]
* [[Chris J. Thornton]] ('''1990'''). ''[https://link.springer.com/chapter/10.1007/978-94-009-0643-3_155 The Kink Representation for Exclusive-OR]''. [https://link.springer.com/book/10.1007/978-94-009-0643-3 International Neural Network Conference]
* [[Gordon Goetsch]] ('''1990'''). ''Maximization of Mutual Information in a Context Sensitive Neural Network''. Ph.D. thesis
* [[Vadim Anshelevich]] ('''1990'''). ''Neural Networks''. Review. in Multi Component Systems (Russian)
'''2004'''
* [http://dblp.uni-trier.de/pers/hd/p/Patist:Jan_Peter Jan Peter Patist], [[Marco Wiering]] ('''2004'''). ''Learning to Play Draughts using Temporal Difference Learning with Neural Networks and Databases''. [http://students.uu.nl/en/hum/cognitive-artificial-intelligence Cognitive Artificial Intelligence], [https://en.wikipedia.org/wiki/Utrecht_University Utrecht University], Benelearn’04
* [[Henk Mannen]], [[Marco Wiering]] ('''2004'''). ''[https://www.semanticscholar.org/paper/Learning-to-Play-Chess-using-TD(lambda)-learning-Mannen-Wiering/00a6f81c8ebe8408c147841f26ed27eb13fb07f3 Learning to play chess using TD(λ)-learning with database games]''. [http://students.uu.nl/en/hum/cognitive-artificial-intelligence Cognitive Artificial Intelligence], [https://en.wikipedia.org/wiki/Utrecht_University Utrecht University], Benelearn’04, [https://www.ai.rug.nl/~mwiering/GROUP/ARTICLES/learning-chess.pdf pdf]
* [[Mathieu Autonès]], [[Aryel Beck]], [[Phillippe Camacho]], [[Nicolas Lassabe]], [[Hervé Luga]], [[François Scharffe]] ('''2004'''). ''[http://link.springer.com/chapter/10.1007/978-3-540-24650-3_1 Evaluation of Chess Position by Modular Neural network Generated by Genetic Algorithm]''. [http://www.informatik.uni-trier.de/~ley/db/conf/eurogp/eurogp2004.html#AutonesBCLLS04 EuroGP 2004] <ref>[https://www.stmintz.com/ccc/index.php?id=358770 Presentation for a neural net learning chess program] by [[Dann Corbit]], [[CCC]], April 06, 2004</ref>
* [[Daniel Walker]], [[Robert Levinson]] ('''2004'''). ''The MORPH Project in 2004''. [[ICGA Journal#27_4|ICGA Journal, Vol. 27, No. 4]]
* [[Christopher Clark]], [[Amos Storkey]] ('''2014'''). ''Teaching Deep Convolutional Neural Networks to Play Go''. [http://arxiv.org/abs/1412.3409 arXiv:1412.3409] <ref>[http://computer-go.org/pipermail/computer-go/2014-December/007010.html Teaching Deep Convolutional Neural Networks to Play Go] by [[Hiroshi Yamashita]], [http://computer-go.org/pipermail/computer-go/ The Computer-go Archives], December 14, 2014</ref> <ref>[http://www.talkchess.com/forum/viewtopic.php?t=54663 Teaching Deep Convolutional Neural Networks to Play Go] by [[Michel Van den Bergh]], [[CCC]], December 16, 2014</ref>
* [[Chris J. Maddison]], [[Shih-Chieh Huang|Aja Huang]], [[Ilya Sutskever]], [[David Silver]] ('''2014'''). ''Move Evaluation in Go Using Deep Convolutional Neural Networks''. [http://arxiv.org/abs/1412.6564v1 arXiv:1412.6564v1] » [[Go]]
* [[Ilya Sutskever]], [https://research.google.com/pubs/OriolVinyals.html [Oriol Vinyals]], [https://www.linkedin.com/in/quoc-v-le-319b5a8 [Quoc V. Le]] ('''2014'''). ''Sequence to Sequence Learning with Neural Networks''. [https://arxiv.org/abs/1409.3215 arXiv:1409.3215]
'''2015'''
* [https://scholar.google.nl/citations?user=yyIoQu4AAAAJ Diederik P. Kingma], [https://scholar.google.ca/citations?user=ymzxRhAAAAAJ&hl=en Jimmy Lei Ba] ('''2015'''). ''Adam: A Method for Stochastic Optimization''. [https://arxiv.org/abs/1412.6980v8 arXiv:1412.6980v8], [http://www.iclr.cc/doku.php?id=iclr2015:main ICLR 2015] <ref>[http://www.talkchess.com/forum/viewtopic.php?t=61948 Arasan 19.2] by [[Jon Dart]], [[CCC]], November 03, 2016 » [[Arasan#Tuning|Arasan's Tuning]]</ref>
* [http://michaelnielsen.org/ Michael Nielsen] ('''2015'''). ''[http://neuralnetworksanddeeplearning.com/ Neural networks and deep learning]''. Determination Press
* [[Mathematician#SIoffe|Sergey Ioffe]], [[Mathematician#CSzegedy|Christian Szegedy]] ('''2015'''). ''Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift''. [https://arxiv.org/abs/1502.03167 arXiv:1502.03167]
* [[Mathematician#GEHinton|Geoffrey E. Hinton]], [https://research.google.com/pubs/OriolVinyals.html [Oriol Vinyals]], [https://en.wikipedia.org/wiki/Jeff_Dean_(computer_scientist) Jeff Dean] ('''2015'''). ''Distilling the Knowledge in a Neural Network''. [https://arxiv.org/abs/1503.02531 arXiv:1503.02531]
* [[James L. McClelland]] ('''2015'''). ''[https://web.stanford.edu/group/pdplab/pdphandbook/handbook3.html#handbookch10.html Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises]''. Second Edition, [https://web.stanford.edu/group/pdplab/pdphandbook/handbookli1.html Contents]
* [[Gábor Melis]] ('''2015'''). ''[http://jmlr.org/proceedings/papers/v42/meli14.html Dissecting the Winning Solution of the HiggsML Challenge]''. [https://nips.cc/Conferences/2014 NIPS 2014]
* [[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]
* [[Douglas Bagnall]] ('''2015'''). ''Author Identification using Multi-headed Recurrent Neural Networks''. [https://arxiv.org/abs/1506.04891 arXiv:1506.04891]
* [[Guillaume Desjardins]], [[Karen Simonyan]], [[Mathematician#RPascanu|Razvan Pascanu]], [[Koray Kavukcuoglu]] ('''2015'''). ''Natural Neural Networks''. [https://arxiv.org/abs/1507.00210 arXiv:1507.00210]
* [[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>
* [[Matthew Lai]] ('''2015'''). ''Giraffe: Using Deep Reinforcement Learning to Play Chess''. M.Sc. thesis, [https://en.wikipedia.org/wiki/Imperial_College_London Imperial College London], [http://arxiv.org/abs/1509.01549v1 arXiv:1509.01549v1] » [[Giraffe]]
* [[Nikolai Yakovenko]], [[Liangliang Cao]], [[Colin Raffel]], [[James Fan]] ('''2015'''). ''Poker-CNN: A Pattern Learning Strategy for Making Draws and Bets in Poker Games''. [https://arxiv.org/abs/1509.06731 arXiv:1509.06731]
* [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] <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]]
* [[Audrūnas Gruslys]], [[Rémi Munos]], [[Ivo Danihelka]], [[Marc Lanctot]], [[Alex Graves]] ('''2016'''). ''Memory-Efficient Backpropagation Through Time''. [https://arxiv.org/abs/1606.03401v1 arXiv:1606.03401]
* [[Mathematician#AARusu|Andrei A. Rusu]], [[Neil C. Rabinowitz]], [[Guillaume Desjardins]], [[Hubert Soyer]], [[James Kirkpatrick]], [[Koray Kavukcuoglu]], [[Mathematician#RPascanu|Razvan Pascanu]], [[Mathematician#RHadsell|Raia Hadsell]] ('''2016'''). ''Progressive Neural Networks''. [https://arxiv.org/abs/1606.04671 arXiv:1606.04671]
* [[Douglas Bagnall]] ('''2016'''). ''Authorship clustering using multi-headed recurrent neural networks''. [https://arxiv.org/abs/1608.04485 arXiv:1608.04485]
* [[Gao Huang]], [[Zhuang Liu]], [[Laurens van der Maaten]], [[Kilian Q. Weinberger]] ('''2016'''). ''Densely Connected Convolutional Networks''. [https://arxiv.org/abs/1608.06993 arXiv:1608.06993] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=2&t=75665&start=9 Re: Minic version 3] by [[Connor McMonigle]], [[CCC]], November 03, 2020 » [[Minic#Minic 3|Minic 3]], [[Seer|Seer 1.1]]</ref>
* [[George Rajna]] ('''2016'''). ''Deep Neural Networks''. [http://vixra.org/abs/1609.0126 viXra:1609.0126]
* [[James Kirkpatrick]], [[Mathematician#RPascanu|Razvan Pascanu]], [[Neil C. Rabinowitz]], [[Joel Veness]], [[Guillaume Desjardins]], [[Mathematician#AARusu|Andrei A. Rusu]], [[Kieran Milan]], [[John Quan]], [[Tiago Ramalho]], [[Agnieszka Grabska-Barwinska]], [[Demis Hassabis]], [[Claudia Clopath]], [[Dharshan Kumaran]], [[Mathematician#RHadsell|Raia Hadsell]] ('''2016'''). ''Overcoming catastrophic forgetting in neural networks''. [https://arxiv.org/abs/1612.00796 arXiv:1612.00796] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=7&t=70704 catastrophic forgetting] by [[Daniel Shawul]], [[CCC]], May 09, 2019</ref>
* [[George Rajna]] ('''2017'''). ''Artificial Neural Network''. [http://vixra.org/abs/1702.0130 viXra:1702.0130]
* [[Raúl Rojas]] ('''2017'''). ''Deepest Neural Networks''. [https://arxiv.org/abs/1707.02617 arXiv:1707.02617]
* [[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
* [[Xinqi Zhu]], [[Michael Bain]] ('''2017'''). ''B-CNN: Branch Convolutional Neural Network for Hierarchical Classification''. [https://arxiv.org/abs/1709.09890 arXiv:1709.09890], [https://github.com/zhuxinqimac/B-CNN GitHub - zhuxinqimac/B-CNN: Sample code of B-CNN paper]
* [[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>
* [[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 <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>
* [[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] » [[Neural Networks#AlphaZero|AlphaZero]] <ref>[http://www.talkchess.com/forum/viewtopic.php?t=65909 Google's AlphaGo team has been working on chess] by [[Peter Kappler]], [[CCC]], December 06, 2017</ref>
* [https://dblp.org/pers/hd/s/Serb:Alexander Alexantrou Serb], [[Edoardo Manino]], [https://dblp.org/pers/hd/m/Messaris:Ioannis Ioannis Messaris], [https://dblp.org/pers/hd/t/Tran=Thanh:Long Long Tran-Thanh], [https://www.orc.soton.ac.uk/people/tp1f12 Themis Prodromakis] ('''2017'''). ''[https://eprints.soton.ac.uk/425616/ Hardware-level Bayesian inference]''. [https://nips.cc/Conferences/2017 NIPS 2017] » [[Analog Evaluation]]
'''2018'''
* [[Yu Nasu]] ('''2018'''). ''ƎUИИ Efficiently Updatable Neural-Network based Evaluation Functions for Computer Shogi''. Ziosoft Computer Shogi Club, [https://github.com/ynasu87/nnue/blob/master/docs/nnue.pdf pdf], [https://www.apply.computer-shogi.org/wcsc28/appeal/the_end_of_genesis_T.N.K.evolution_turbo_type_D/nnue.pdf pdf] (Japanese with English abstract) [https://github.com/asdfjkl/nnue GitHub - asdfjkl/nnue translation] » [[NNUE]]<ref>[http://www.talkchess.com/forum3/viewtopic.php?f=2&t=76250 Translation of Yu Nasu's NNUE paper] by [[Dominik Klein]], [[CCC]], January 07, 2021</ref>
* [[Kei Takada]], [[Hiroyuki Iizuka]], [[Masahito Yamamoto]] ('''2018'''). ''[https://link.springer.com/chapter/10.1007%2F978-3-319-75931-9_2 Computer Hex Algorithm Using a Move Evaluation Method Based on a Convolutional Neural Network]''. [https://link.springer.com/bookseries/7899 Communications in Computer and Information Science] » [[Hex]]
* [[Matthia Sabatelli]], [[Francesco Bidoia]], [[Valeriu Codreanu]], [[Marco Wiering]] ('''2018'''). ''Learning to Evaluate Chess Positions with Deep Neural Networks and Limited Lookahead''. ICPRAM 2018, [http://www.ai.rug.nl/~mwiering/GROUP/ARTICLES/ICPRAM_CHESS_DNN_2018.pdf pdf]
* [[Marius Lindauer]], [[Frank Hutter]] ('''2019'''). ''Best Practices for Scientific Research on Neural Architecture Search''. [https://arxiv.org/abs/1909.02453 arXiv:1909.02453]
* [[Guy Haworth]] ('''2019'''). ''Chess endgame news: an endgame challenge for neural nets''. [[ICGA Journal#41_3|ICGA Journal, Vol. 41, No. 3]] » [[Endgame]]
* [https://scholar.google.co.uk/citations?user=JNTc6R4AAAAJ&hl=en Philip G. Breen], [https://scholar.google.com/citations?user=fJmka-IAAAAJ&hl=en Christopher N. Foley], [https://scholar.google.com/citations?user=QOehl_0AAAAJ&hl=en Tjarda Boekholt], [[Simon Portegies Zwart]] ('''2019'''). ''Newton vs the machine: solving the chaotic three-body problem using deep neural networks''. [https://arxiv.org/abs/1910.07291 arXiv:1910.07291]
==2020 ...==
* [[Reid McIlroy-Young]], [[Siddhartha Sen]], [[Jon Kleinberg]], [[Ashton Anderson]] ('''2020'''). ''Aligning Superhuman AI with Human Behavior: Chess as a Model System''. [[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]
* [[Oisín Carroll]], [[Joeran Beel]] ('''2020'''). ''Finite Group Equivariant Neural Networks for Games''. [https://arxiv.org/abs/2009.05027 arXiv:2009.05027]
* [https://scholar.google.com/citations?user=HT85tXsAAAAJ&hl=en Mohammad Pezeshki], [https://scholar.google.com/citations?user=jKqh8jAAAAAJ&hl=en Sékou-Oumar Kaba], [[Mathematician#YBengio|Yoshua Bengio]] , [[Mathematician#ACourville|Aaron Courville]] , [[Doina Precup]], [https://scholar.google.com/citations?user=ifu_7_0AAAAJ&hl=en Guillaume Lajoie] ('''2020'''). ''Gradient Starvation: A Learning Proclivity in Neural Networks''. [https://arxiv.org/abs/2011.09468 arXiv:2011.09468]
* [[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]]
'''2021'''
* [[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>
=Blog & Forum Posts=
==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=74531 NNUE accessible explanation] by [[Martin Fierz]], [[CCC]], July 21, 2020 » [[NNUE]]
: [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74531&start=1 Re: NNUE accessible explanation] by [[Jonathan Rosenthal]], [[CCC]], July 23, 2020
: [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74531&start=5 Re: NNUE accessible explanation] by [[Jonathan Rosenthal]], [[CCC]], July 24, 2020
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74777 Neural Networks weights type] by [[Fabio Gobbato]], [[CCC]], August 13, 2020 » [[Stockfish NNUE]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74955 Train a neural network evaluation] by [[Fabio Gobbato]], [[CCC]], September 01, 2020 » [[Automated Tuning]], [[NNUE]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=75042 Neural network quantization] by [[Fabio Gobbato]], [[CCC]], September 08, 2020 » [[NNUE]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=75190 First success with neural nets] by [[Jonathan Kreuzer]], [[CCC]], September 23, 2020
* [http://www.talkchess.com/forum3/viewtopic.php?f=2&t=75606 Transhuman Chess with NN and RL...] by [[Srdja Matovic]], [[CCC]], October 30, 2020 » [[Reinforcement Learning|RL]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=75724 Pytorch NNUE training] by [[Gary Linscott]], [[CCC]], November 08, 2020 <ref>[https://en.wikipedia.org/wiki/PyTorch PyTorch from Wikipedia]</ref> » [[NNUE]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=75925 Pawn King Neural Network] by [[Tamás Kuzmics]], [[CCC]], November 26, 2020 » [[NNUE]]
* [http://laatste.info/bb3/viewtopic.php?f=53&t=8327 Learning draughts evaluation functions using Keras/TensorFlow] by [[Rein Halbersma]], [http://laatste.info/bb3/viewforum.php?f=53 World Draughts Forum], November 30, 2020 » [[Draughts]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=75985 Maiachess] by [[Marc-Philippe Huget]], [[CCC]], December 04, 2020 » [[Maia Chess]]
'''2021'''
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=76263 More experiments with neural nets] by [[Jonathan Kreuzer]], [[CCC]], January 09, 2021 » [[Slow Chess]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=76334 Keras/Tensforflow for very sparse inputs] by Jacek Dermont, [[CCC]], January 16, 2021
* [http://www.talkchess.com/forum3/viewtopic.php?f=2&t=76664 Are neural nets (the weights file) copyrightable?] by [[Adam Treat]], [[CCC]], February 21, 2021
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=76885 A worked example of backpropagation using Javascript] by [[Colin Jenkins]], [[CCC]], March 16, 2021 » [[Neural Networks#Backpropagation|Backpropagation]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=77061 yet another NN library] by lucasart, [[CCC]], April 11, 2021 » [[#lucasart|lucasart/nn]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=77492 Some more experiments with neural nets] by [[Jonathan Kreuzer]], [[CCC]], June 15, 2021 » [[Slow Chess]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=2&t=77605&start=55 Re: Stockfish 14 has been released] by [[Connor McMonigle]], [[CCC]], July 04, 2021 » [[Stockfish]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=77899 tablebase neural nets] by [[Robert Pope]], [[CCC]], August 07, 2021 » [[Endgame Tablebases]]
* [https://www.talkchess.com/forum3/viewtopic.php?f=2&t=78283 Book about Neural Networks for Chess] by dkl, [[CCC]], September 29, 2021
'''2022'''
* [https://www.talkchess.com/forum3/viewtopic.php?f=7&t=79332 Binary Neural Networks Sliding Piece Inference <nowiki>[Release]</nowiki>] by [[Daniel Infuehr]], [[CCC]], February 10, 2022 » [[Sliding Piece Attacks]]
* [https://www.talkchess.com/forum3/viewtopic.php?f=7&t=79368 Failure of trivial approach to neural network move ordering] by [[Jost Triller]], [[CCC]], February 16, 2022 » [[Move Ordering]]
=External Links=
* [https://en.wikipedia.org/wiki/Rprop Rprop from Wikipedia]
* [http://people.idsia.ch/~juergen/who-invented-backpropagation.html Who Invented Backpropagation?] by [[Jürgen Schmidhuber]] (2014, 2015)
* [https://alexander-schiendorfer.github.io/2020/02/24/a-worked-example-of-backprop.html A worked example of backpropagation] by [https://alexander-schiendorfer.github.io/about.html Alexander Schiendorfer], February 24, 2020 » [[Neural Networks#Backpropagation|Backpropagation]] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=7&t=76885 A worked example of backpropagation using Javascript] by [[Colin Jenkins]], [[CCC]], March 16, 2021</ref>
==Gradient==
* [https://en.wikipedia.org/wiki/Gradient Gradient from Wikipedia]
: [https://en.wikipedia.org/wiki/SNNS SNNS from Wikipedia]
* [https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software Comparison of deep learning software from Wikipedia]
* [https://github.com/connormcmonigle/reference-neural-network GitHub - connormcmonigle/reference-neural-network] by [[Connor McMonigle]]
* <span id="lucasart"></span>[https://github.com/lucasart/nn GitHub - lucasart/nn: neural network experiment] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=7&t=77061 yet another NN library] by lucasart, [[CCC]], April 11, 2021</ref>
==Libraries==
* [https://en.wikipedia.org/wiki/Eigen_%28C%2B%2B_library%29 Eigen (C++ library) from Wikipedia]
* [http://leenissen.dk/fann/wp/ Fast Artificial Neural Network Library (FANN)]
* [https://en.wikipedia.org/wiki/Keras Keras from Wikipedia]
* [https://wiki.python.org/moin/PythonForArtificialIntelligence PythonForArtificialIntelligence - Python Wiki] [[Python]]
* [https://en.wikipedia.org/wiki/TensorFlow TensorFlow from Wikipedia]
: [https://www.youtube.com/watch?v=lvoHnicueoE Lecture 14 | Deep Reinforcement Learning] by [[Mathematician#SYeung|Serena Yeung]], [http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture14.pdf slides]
: [https://www.youtube.com/watch?v=eZdOkDtYMoo Lecture 15 | Efficient Methods and Hardware for Deep Learning] by [https://scholar.google.com/citations?user=E0iCaa4AAAAJ&hl=en Song Han], [http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture15.pdf slides]
=References=
<references />
'''[[Learning|Up one Level]]'''
[[Category:Videos]]
