Difference between revisions of "Deep Learning"

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==1990 ...==
 
==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>
 
* [[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>
 
* [[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 ...==
 
==2000 ...==
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==2010 ...==
 
==2010 ...==
 
* [https://scholar.google.ca/citations?user=tJ_PrzgAAAAJ&hl=en Abdelrahman Mohamed], [https://scholar.google.ca/citations?user=ghbWy-0AAAAJ&hl=en George E. Dahl], [[Mathematician#GEHinton|Geoffrey E. Hinton]]  ('''2011'''). ''Acoustic Modeling using Deep Belief Networks''. [[IEEE#TSP|IEEE Transactions on Audio, Speech, and Language Processing]], Vol. 20, No. 1, [http://www.cs.toronto.edu/~asamir/papers/speechDBN_jrnl.pdf pdf]
 
* [https://scholar.google.ca/citations?user=tJ_PrzgAAAAJ&hl=en Abdelrahman Mohamed], [https://scholar.google.ca/citations?user=ghbWy-0AAAAJ&hl=en George E. Dahl], [[Mathematician#GEHinton|Geoffrey E. Hinton]]  ('''2011'''). ''Acoustic Modeling using Deep Belief Networks''. [[IEEE#TSP|IEEE Transactions on Audio, Speech, and Language Processing]], Vol. 20, No. 1, [http://www.cs.toronto.edu/~asamir/papers/speechDBN_jrnl.pdf pdf]
* [https://en.wikipedia.org/wiki/Yoshua_Bengio Yoshua Bengio] ('''2012'''). ''Deep Learning of Representations for Unsupervised and Transfer Learning''. [http://www.jmlr.org/proceedings/papers/v27/ JMLR: Workshop on Unsupervised and Transfer Learning, 2011], [http://www.jmlr.org/proceedings/papers/v27/bengio12a/bengio12a.pdf pdf]
+
* [[Mathematician#YBengio|Yoshua Bengio]] ('''2012'''). ''Deep Learning of Representations for Unsupervised and Transfer Learning''. [http://www.jmlr.org/proceedings/papers/v27/ JMLR: Workshop on Unsupervised and Transfer Learning, 2011], [http://www.jmlr.org/proceedings/papers/v27/bengio12a/bengio12a.pdf pdf]
 
'''2013'''
 
'''2013'''
 
* [[Mathematician#GMontavon|Grégoire Montavon]] ('''2013'''). ''[https://opus4.kobv.de/opus4-tuberlin/frontdoor/index/index/docId/4467 On Layer-Wise Representations in Deep Neural Networks]''. Ph.D. Thesis, [https://en.wikipedia.org/wiki/Technical_University_of_Berlin TU Berlin], advisor [[Mathematician#KRMueller|Klaus-Robert Müller]]
 
* [[Mathematician#GMontavon|Grégoire Montavon]] ('''2013'''). ''[https://opus4.kobv.de/opus4-tuberlin/frontdoor/index/index/docId/4467 On Layer-Wise Representations in Deep Neural Networks]''. Ph.D. Thesis, [https://en.wikipedia.org/wiki/Technical_University_of_Berlin TU Berlin], advisor [[Mathematician#KRMueller|Klaus-Robert Müller]]
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* [[Jürgen Schmidhuber]] ('''2013'''). ''My First Deep Learning System of 1991 + Deep Learning Timeline 1962-2013''. [https://arxiv.org/abs/1312.5548 arXiv:1312.5548]
 
* [[Jürgen Schmidhuber]] ('''2013'''). ''My First Deep Learning System of 1991 + Deep Learning Timeline 1962-2013''. [https://arxiv.org/abs/1312.5548 arXiv:1312.5548]
 
'''2014'''
 
'''2014'''
* [[Ian Goodfellow]], [[Jean Pouget-Abadie]], [[Mehdi Mirza]], [[Bing Xu]], [[David Warde-Farley]], [[Sherjil Ozair]], [[Aaron Courville]], [[Yoshua Bengio]] ('''2014'''). ''Generative Adversarial Networks''. [https://arxiv.org/abs/1406.2661v1 arXiv:1406.2661v1]
+
* [[Mathematician#IGoodfellow|Ian Goodfellow]], [[Jean Pouget-Abadie]], [[Mehdi Mirza]], [[Bing Xu]], [[David Warde-Farley]], [[Sherjil Ozair]], [[Mathematician#ACourville|Aaron Courville]], [[Mathematician#YBengio|Yoshua Bengio]] ('''2014'''). ''Generative Adversarial Networks''. [https://arxiv.org/abs/1406.2661v1 arXiv:1406.2661v1]
 
* [[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>[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>[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#KCho|Kyunghyun Cho]] ('''2014'''). ''[https://aaltodoc.aalto.fi/handle/123456789/12729 Foundations and Advances in Deep Learning]''. Ph.D. thesis, [https://en.wikipedia.org/wiki/Aalto_University Aalto University], supervisor [[Mathematician#JKarhunen|Juha Karhunen]]
 
* [[Mathematician#KCho|Kyunghyun Cho]] ('''2014'''). ''[https://aaltodoc.aalto.fi/handle/123456789/12729 Foundations and Advances in Deep Learning]''. Ph.D. thesis, [https://en.wikipedia.org/wiki/Aalto_University Aalto University], supervisor [[Mathematician#JKarhunen|Juha Karhunen]]
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* [[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]
 
* [[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://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>
 
* [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]], [[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
+
* [[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]
 
* [[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]]
 
* [[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]]
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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
 
* [[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]
 
* [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://cs231n.stanford.edu/reports/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>
+
* [[Barak Oshri]], [[Nishith Khandwala]] ('''2015'''). ''Predicting Moves in Chess using Convolutional Neural Networks''. [http://vision.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>
* [[Yann LeCun]], [[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>
+
* [[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]]
 
* [[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]]
 
* [https://scholar.google.com/citations?user=DhtAFkwAAAAJ Kaiming He], [https://scholar.google.com/citations?user=yuB-cfoAAAAJ&hl=en Xiangyu Zhang], [http://shaoqingren.com/ Shaoqing Ren], [http://www.jiansun.org/ Jian Sun] ('''2015'''). ''Deep Residual Learning for Image Recognition''. [https://arxiv.org/abs/1512.03385 arXiv:1512.03385]
 
* [https://scholar.google.com/citations?user=DhtAFkwAAAAJ Kaiming He], [https://scholar.google.com/citations?user=yuB-cfoAAAAJ&hl=en Xiangyu Zhang], [http://shaoqingren.com/ Shaoqing Ren], [http://www.jiansun.org/ Jian Sun] ('''2015'''). ''Deep Residual Learning for Image Recognition''. [https://arxiv.org/abs/1512.03385 arXiv:1512.03385]
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* [[Eli David|Omid E. David]], [[Nathan S. Netanyahu]], [[Lior Wolf]] ('''2016'''). ''[http://link.springer.com/chapter/10.1007%2F978-3-319-44781-0_11 DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess]''. [http://icann2016.org/ ICAAN 2016], [https://en.wikipedia.org/wiki/Lecture_Notes_in_Computer_Science Lecture Notes in Computer Science], Vol. 9887, [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer], [http://www.cs.tau.ac.il/~wolf/papers/deepchess.pdf pdf preprint] » [[DeepChess]] <ref>[http://www.talkchess.com/forum/viewtopic.php?t=61748 DeepChess: Another deep-learning based chess program] by [[Matthew Lai]], [[CCC]], October 17, 2016</ref> <ref>[http://icann2016.org/index.php/conference-programme/recipients-of-the-best-paper-awards/ ICANN 2016 | Recipients of the best paper awards]</ref>
 
* [[Eli David|Omid E. David]], [[Nathan S. Netanyahu]], [[Lior Wolf]] ('''2016'''). ''[http://link.springer.com/chapter/10.1007%2F978-3-319-44781-0_11 DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess]''. [http://icann2016.org/ ICAAN 2016], [https://en.wikipedia.org/wiki/Lecture_Notes_in_Computer_Science Lecture Notes in Computer Science], Vol. 9887, [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer], [http://www.cs.tau.ac.il/~wolf/papers/deepchess.pdf pdf preprint] » [[DeepChess]] <ref>[http://www.talkchess.com/forum/viewtopic.php?t=61748 DeepChess: Another deep-learning based chess program] by [[Matthew Lai]], [[CCC]], October 17, 2016</ref> <ref>[http://icann2016.org/index.php/conference-programme/recipients-of-the-best-paper-awards/ ICANN 2016 | Recipients of the best paper awards]</ref>
 
* [[Dror Sholomon]], [[Eli David|Omid E. David]], [[Nathan S. Netanyahu]] ('''2016'''). ''[http://link.springer.com/chapter/10.1007/978-3-319-44781-0_21 DNN-Buddies: A Deep Neural Network-Based Estimation Metric for the Jigsaw Puzzle Problem]''. [http://icann2016.org/ ICAAN 2016], [https://en.wikipedia.org/wiki/Lecture_Notes_in_Computer_Science Lecture Notes in Computer Science], Vol. 9887, [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer] <ref>[https://en.wikipedia.org/wiki/Jigsaw_puzzle Jigsaw puzzle from Wikipedia]</ref>
 
* [[Dror Sholomon]], [[Eli David|Omid E. David]], [[Nathan S. Netanyahu]] ('''2016'''). ''[http://link.springer.com/chapter/10.1007/978-3-319-44781-0_21 DNN-Buddies: A Deep Neural Network-Based Estimation Metric for the Jigsaw Puzzle Problem]''. [http://icann2016.org/ ICAAN 2016], [https://en.wikipedia.org/wiki/Lecture_Notes_in_Computer_Science Lecture Notes in Computer Science], Vol. 9887, [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer] <ref>[https://en.wikipedia.org/wiki/Jigsaw_puzzle Jigsaw puzzle from Wikipedia]</ref>
* [[Ian Goodfellow]], [[Yoshua Bengio]], [[Aaron Courville]] ('''2016'''). ''[http://www.deeplearningbook.org/ Deep Learning]''. [https://en.wikipedia.org/wiki/MIT_Press MIT Press]
+
* [[Mathematician#IGoodfellow|Ian Goodfellow]], [[Mathematician#YBengio|Yoshua Bengio]], [[Mathematician#ACourville|Aaron Courville]] ('''2016'''). ''[http://www.deeplearningbook.org/ Deep Learning]''. [https://en.wikipedia.org/wiki/MIT_Press MIT Press]
 
* [[Volodymyr Mnih]], [[Adrià Puigdomènech Badia]], [[Mehdi Mirza]], [[Alex Graves]], [[Timothy Lillicrap]], [[Tim Harley]], [[David Silver]], [[Koray Kavukcuoglu]] ('''2016'''). ''Asynchronous Methods for Deep Reinforcement Learning''.  [https://arxiv.org/abs/1602.01783 arXiv:1602.01783v2]
 
* [[Volodymyr Mnih]], [[Adrià Puigdomènech Badia]], [[Mehdi Mirza]], [[Alex Graves]], [[Timothy Lillicrap]], [[Tim Harley]], [[David Silver]], [[Koray Kavukcuoglu]] ('''2016'''). ''Asynchronous Methods for Deep Reinforcement Learning''.  [https://arxiv.org/abs/1602.01783 arXiv:1602.01783v2]
 
* [[Johannes Heinrich]], [[David Silver]] ('''2016'''). ''Deep Reinforcement Learning from Self-Play in Imperfect-Information Games''. [https://arxiv.org/abs/1603.01121 arXiv:1603.01121]  <ref>[https://www.theguardian.com/technology/2016/mar/30/deepmind-poker-alphago-computer-casino?CMP=twt_a-technology_b-gdntech Could DeepMind try to conquer poker next?] by [https://www.theguardian.com/profile/alex-hern Alex Hern], [https://en.wikipedia.org/wiki/The_Guardian The Guardian], March 30, 2016</ref>
 
* [[Johannes Heinrich]], [[David Silver]] ('''2016'''). ''Deep Reinforcement Learning from Self-Play in Imperfect-Information Games''. [https://arxiv.org/abs/1603.01121 arXiv:1603.01121]  <ref>[https://www.theguardian.com/technology/2016/mar/30/deepmind-poker-alphago-computer-casino?CMP=twt_a-technology_b-gdntech Could DeepMind try to conquer poker next?] by [https://www.theguardian.com/profile/alex-hern Alex Hern], [https://en.wikipedia.org/wiki/The_Guardian The Guardian], March 30, 2016</ref>
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* [[Ilya Loshchilov]], [[Frank Hutter]] ('''2016'''). ''CMA-ES for Hyperparameter Optimization of Deep Neural Networks''. [https://arxiv.org/abs/1604.07269 arXiv:1604.07269] <ref>[https://en.wikipedia.org/wiki/CMA-ES CMA-ES from Wikipedia]</ref>
 
* [[Ilya Loshchilov]], [[Frank Hutter]] ('''2016'''). ''CMA-ES for Hyperparameter Optimization of Deep Neural Networks''. [https://arxiv.org/abs/1604.07269 arXiv:1604.07269] <ref>[https://en.wikipedia.org/wiki/CMA-ES CMA-ES from Wikipedia]</ref>
 
* [[Dale Schuurmans]], [[Martin Zinkevich]] ('''2016'''). ''[https://research.google.com/pubs/pub45550.html Deep Learning Games]''. [https://nips.cc/Conferences/2016/Schedule?type=Poster NIPS 2016]
 
* [[Dale Schuurmans]], [[Martin Zinkevich]] ('''2016'''). ''[https://research.google.com/pubs/pub45550.html Deep Learning Games]''. [https://nips.cc/Conferences/2016/Schedule?type=Poster NIPS 2016]
* [[Andrei A. Rusu]], [[Neil C. Rabinowitz]], [[Guillaume Desjardins]], [[Hubert Soyer]], [[James Kirkpatrick]], [[Koray Kavukcuoglu]], [[Razvan Pascanu]], [[Raia Hadsell]] ('''2016'''). ''Progressive Neural Networks''. [https://arxiv.org/abs/1606.04671 arXiv:1606.04671]
+
* [[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]
 
* [[Ilya Loshchilov]], [[Frank Hutter]] ('''2016'''). ''SGDR: Stochastic Gradient Descent with Warm Restarts''. [https://arxiv.org/abs/1608.03983 arXiv:1608.03983]
 
* [[Ilya Loshchilov]], [[Frank Hutter]] ('''2016'''). ''SGDR: Stochastic Gradient Descent with Warm Restarts''. [https://arxiv.org/abs/1608.03983 arXiv:1608.03983]
 
* [[Shixiang Gu]], [[Ethan Holly]], [[Timothy Lillicrap]], [[Sergey Levine]] ('''2016'''). ''Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates''. [https://arxiv.org/abs/1610.00633 arXiv:1610.00633]
 
* [[Shixiang Gu]], [[Ethan Holly]], [[Timothy Lillicrap]], [[Sergey Levine]] ('''2016'''). ''Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates''. [https://arxiv.org/abs/1610.00633 arXiv:1610.00633]
 
* [[Jane X Wang]], [[Zeb Kurth-Nelson]], [[Dhruva Tirumala]], [[Hubert Soyer]], [[Joel Z Leibo]], [[Rémi Munos]], [[Charles Blundell]], [[Dharshan Kumaran]], [[Matthew Botvinick]] ('''2016'''). ''Learning to reinforcement learn''. [https://arxiv.org/abs/1611.05763 arXiv:1611.05763]
 
* [[Jane X Wang]], [[Zeb Kurth-Nelson]], [[Dhruva Tirumala]], [[Hubert Soyer]], [[Joel Z Leibo]], [[Rémi Munos]], [[Charles Blundell]], [[Dharshan Kumaran]], [[Matthew Botvinick]] ('''2016'''). ''Learning to reinforcement learn''. [https://arxiv.org/abs/1611.05763 arXiv:1611.05763]
 
* [[Jonathan Rosenthal]] ('''2016'''). ''[https://www.research-collection.ethz.ch/handle/20.500.11850/156354 Deep Learning for Go]''. B.Sc. thesis,  [[ETH Zurich]]
 
* [[Jonathan Rosenthal]] ('''2016'''). ''[https://www.research-collection.ethz.ch/handle/20.500.11850/156354 Deep Learning for Go]''. B.Sc. thesis,  [[ETH Zurich]]
* [[James Kirkpatrick]], [[Razvan Pascanu]], [[Neil C. Rabinowitz]], [[Joel Veness]], [[Guillaume Desjardins]], [[Andrei A. Rusu]], [[Kieran Milan]], [[John Quan]], [[Tiago Ramalho]],  [[Agnieszka Grabska-Barwinska]], [[Demis Hassabis]], [[Claudia Clopath]], [[Dharshan Kumaran]], [[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>
+
* [[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>
 
* [https://dblp.uni-trier.de/pers/hd/n/Niu:Zhenxing Zhenxing Niu], [https://dblp.uni-trier.de/pers/hd/z/Zhou:Mo Mo Zhou], [https://dblp.uni-trier.de/pers/hd/w/Wang_0003:Le Le Wang], [[Xinbo Gao]], [https://dblp.uni-trier.de/pers/hd/h/Hua_0001:Gang Gang Hua] ('''2016'''). ''Ordinal Regression with Multiple Output CNN for Age Estimation''. [https://dblp.uni-trier.de/db/conf/cvpr/cvpr2016.html CVPR 2016], [https://www.cv-foundation.org/openaccess/content_cvpr_2016/app/S21-20.pdf pdf]
 
* [https://dblp.uni-trier.de/pers/hd/n/Niu:Zhenxing Zhenxing Niu], [https://dblp.uni-trier.de/pers/hd/z/Zhou:Mo Mo Zhou], [https://dblp.uni-trier.de/pers/hd/w/Wang_0003:Le Le Wang], [[Xinbo Gao]], [https://dblp.uni-trier.de/pers/hd/h/Hua_0001:Gang Gang Hua] ('''2016'''). ''Ordinal Regression with Multiple Output CNN for Age Estimation''. [https://dblp.uni-trier.de/db/conf/cvpr/cvpr2016.html CVPR 2016], [https://www.cv-foundation.org/openaccess/content_cvpr_2016/app/S21-20.pdf pdf]
* [[Li Jing]], [[Yichen Shen]], [[Tena Dubček]], [[John Peurifoy]], [[Scott Skirlo]], [[Yann LeCun]], [[Max Tegmark]], [[Marin Soljačić]] ('''2016'''). ''Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs''. [https://arxiv.org/abs/1612.05231 arXiv:1612.05231] <ref>[http://talkchess.com/forum3/viewtopic.php?f=2&t=74059 Stockfish NN release (NNUE)] by [[Henk Drost]], [[CCC]], May 31, 2020 » [[Stockfish]]</ref>
+
* [[Li Jing]], [[Yichen Shen]], [[Tena Dubček]], [[John Peurifoy]], [[Scott Skirlo]], [[Mathematician#YLeCun|Yann LeCun]], [[Max Tegmark]], [[Marin Soljačić]] ('''2016'''). ''Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs''. [https://arxiv.org/abs/1612.05231 arXiv:1612.05231] <ref>[http://talkchess.com/forum3/viewtopic.php?f=2&t=74059 Stockfish NN release (NNUE)] by [[Henk Drost]], [[CCC]], May 31, 2020 » [[Stockfish]]</ref>
 
'''2017'''
 
'''2017'''
 
* [[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]
 
* [[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]
 
* [[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]
 
* [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]
 
* [[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]
 
* [[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]
 
* [[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]
* [[Matej Moravčík]], [[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]]
 
 
* [[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]
 
* [[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]
 
* [[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]], [[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]
 
* [[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]
* [[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>
 
* [[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]
 
* [[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]]
 
* [[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>
 
* [[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]
 
* [[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>
 
 
* [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]
 
* [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'''
 
'''2018'''
Line 136: Line 143:
 
* [[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]
 
* [[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]
 
* [[Diogo R. Ferreira]], [https://www.researchgate.net/profile/P_Carvalho2 Pedro J. Carvalho], [https://scholar.google.be/citations?user=fkoOZ80AAAAJ&hl=en Horácio Fernandes], [https://en.wikipedia.org/wiki/Joint_European_Torus JET] Contributors ('''2018'''). ''Full-pulse Tomographic Reconstruction with Deep Neural Networks''. [https://arxiv.org/abs/1802.02242 arXiv:1802.02242]
 
* [[Diogo R. Ferreira]], [https://www.researchgate.net/profile/P_Carvalho2 Pedro J. Carvalho], [https://scholar.google.be/citations?user=fkoOZ80AAAAJ&hl=en Horácio Fernandes], [https://en.wikipedia.org/wiki/Joint_European_Torus JET] Contributors ('''2018'''). ''Full-pulse Tomographic Reconstruction with Deep Neural Networks''. [https://arxiv.org/abs/1802.02242 arXiv:1802.02242]
 +
* [[Aditya Rawal]], [[Risto Miikkulainen]] ('''2018'''). ''From Nodes to Networks: Evolving Recurrent Neural Networks''. [https://arxiv.org/abs/1803.04439 arXiv:1803.04439]
 
* [[George Philipp]], [[Jaime Carbonell]] ('''2018'''). ''The Nonlinearity Coefficient - Predicting Generalization in Deep Neural Networks''. [https://arxiv.org/abs/1806.00179 arXiv:1806.00179]
 
* [[George Philipp]], [[Jaime Carbonell]] ('''2018'''). ''The Nonlinearity Coefficient - Predicting Generalization in Deep Neural Networks''. [https://arxiv.org/abs/1806.00179 arXiv:1806.00179]
 
* [[Sai Krishna G.V.]], [[Kyle Goyette]], [[Ahmad Chamseddine]], [[Breandan Considine]] ('''2018'''). ''Deep Pepper: Expert Iteration based Chess agent in the Reinforcement Learning Setting''. [https://arxiv.org/abs/1806.00683 arXiv:1806.00683] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=2&t=67923 Deep Pepper Paper] by Leo, [[CCC]], July 07, 2018</ref>
 
* [[Sai Krishna G.V.]], [[Kyle Goyette]], [[Ahmad Chamseddine]], [[Breandan Considine]] ('''2018'''). ''Deep Pepper: Expert Iteration based Chess agent in the Reinforcement Learning Setting''. [https://arxiv.org/abs/1806.00683 arXiv:1806.00683] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=2&t=67923 Deep Pepper Paper] by Leo, [[CCC]], July 07, 2018</ref>
Line 150: Line 158:
 
* [[David J. Wu]] ('''2019'''). ''Accelerating Self-Play Learning in Go''. [https://arxiv.org/abs/1902.10565 arXiv:1902.10565]
 
* [[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  
 
* [[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=
 
=Forum Posts=
Line 184: Line 218:
 
* [http://www.talkchess.com/forum/viewtopic.php?t=66280 Announcing lczero] by [[Gary Linscott|Gary]], [[CCC]], January 09, 2018 » [[Leela Chess Zero]]
 
* [http://www.talkchess.com/forum/viewtopic.php?t=66280 Announcing lczero] by [[Gary Linscott|Gary]], [[CCC]], January 09, 2018 » [[Leela Chess Zero]]
 
* [http://www.talkchess.com/forum/viewtopic.php?t=66443 Connect 4 AlphaZero implemented using Python...] by [[Steve Maughan]], [[CCC]], January 29, 2018 » [[AlphaZero]], [[Connect Four]], [[Python]]
 
* [http://www.talkchess.com/forum/viewtopic.php?t=66443 Connect 4 AlphaZero implemented using Python...] by [[Steve Maughan]], [[CCC]], January 29, 2018 » [[AlphaZero]], [[Connect Four]], [[Python]]
 +
* [https://groups.google.com/d/msg/lczero/EGcJSrZYLiw/netJ4S38CgAJ use multiple neural nets?] by [[Warren D. Smith]], [[Computer Chess Forums|LCZero Forum]], December 25, 2018 » [[Leela Chess Zero]]
 
'''2019'''
 
'''2019'''
 
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=69942 categorical cross entropy for value] by [[Chris Whittington]], [[CCC]], February 18, 2019
 
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=69942 categorical cross entropy for value] by [[Chris Whittington]], [[CCC]], February 18, 2019
Line 195: Line 230:
 
* [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 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  
 
: [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>
 
* [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 ...==
 
==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=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=
 
=External Links=
Line 230: Line 267:
 
* [https://github.com/fchollet/keras/tree/master/keras keras/keras at master · fchollet/keras · GitHub] » [[Python]]
 
* [https://github.com/fchollet/keras/tree/master/keras keras/keras at master · fchollet/keras · GitHub] » [[Python]]
 
* [https://keras.io/ Keras Documentation]
 
* [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://en.wikipedia.org/wiki/TensorFlow TensorFlow from Wikipedia]
 
* [https://github.com/jtoy/awesome-tensorflow GitHub - jtoy/awesome-tensorflow: TensorFlow]
 
* [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]
 
* [https://en.wikipedia.org/wiki/Theano_(software) Theano (software) from Wikipedia]
 
===Chess===
 
===Chess===
Line 238: Line 277:
 
* [https://github.com/benediamond/leela-chess GitHub - benediamond/leela-chess: A chess adaption of GCP's Leela Zero]  
 
* [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/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>
 
* [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===
 
===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://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://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===
 
===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://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
Line 275: Line 317:
 
* [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>
 
* [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://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==
 
==Videos==
 
* [https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ Deep Learning SIMPLIFIED: The Series Intro]  [https://en.wikipedia.org/wiki/YouTube YouTube] Videos
 
* [https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ Deep Learning SIMPLIFIED: The Series Intro]  [https://en.wikipedia.org/wiki/YouTube YouTube] Videos
* <span id="IlyaSutskeverVideoDeepLearning"></span>Deep Learning Master Class -- Ilya Sutskever, [https://en.wikipedia.org/wiki/YouTube YouTube] Video <ref>[https://www.cs.toronto.edu/~ilya/pubs/ Ilya Sutskever - Publications - Videos of Talks]</ref>
 
: {{#evu:https://www.youtube.com/watch?v=UdSK7nnJKHU|alignment=left|valignment=top}}
 
 
* <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  
 
* <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}}
 
: {{#evu:https://www.youtube.com/watch?v=6bOMf9zr7N8|alignment=left|valignment=top}}

Latest revision as of 11:30, 14 March 2022

Home * Learning * Neural Networks * Deep Learning

Deep Neural Network [1]

Deep Learning,
a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data - characterized as a buzzword, or a rebranding of neural networks. A deep neural network (DNN) is an ANN with multiple hidden layers of units between the input and output layers which can be discriminatively trained with the standard backpropagation algorithm. Two common issues if naively trained are overfitting and computation time. While deep learning techniques have yielded in another breakthrough in computer Go (after Monte-Carlo Tree Search), some trials in computer chess were promising as well, but until December 2017, less spectacular.

Go

Convolutional neural networks form a subclass of feedforward neural networks that have special weight constraints, individual neurons are tiled in such a way that they respond to overlapping regions. Convolutional NNs are suited for deep learning and are highly suitable for parallelization on GPUs [2]. In 2014, two teams independently investigated whether deep convolutional neural networks could be used to directly represent and learn a move evaluation function for the game of Go. Christopher Clark and Amos Storkey trained an 8-layer convolutional neural network by supervised learning from a database of human professional games, which without any search, defeated the traditional search program Gnu Go in 86% of the games [3] [4] [5] [6]. In their paper Move Evaluation in Go Using Deep Convolutional Neural Networks [7], Chris J. Maddison, Aja Huang, Ilya Sutskever, and David Silver report they trained a large 12-layer convolutional neural network in a similar way, to beat Gnu Go in 97% of the games, and matched the performance of a state-of-the-art Monte-Carlo tree search that simulates a million positions per move [8].

In 2015, a team affiliated with Google DeepMind around David Silver and Aja Huang, supported by Google researchers John Nham and Ilya Sutskever, build a Go playing program dubbed AlphaGo [9], combining Monte-Carlo tree search with their 12-layer networks [10].

Chess

Giraffe & Zurichess

In 2015, Matthew Lai trained Giraffe's deep neural network by TD-Leaf [11]. Zurichess by Alexandru Moșoi uses the TensorFlow library for automated tuning - in a two layers neural network, the second layer is responsible for a tapered eval to phase endgame and middlegame scores [12].

DeepChess

In 2016, Omid E. David, Nathan S. Netanyahu, and Lior Wolf introduced DeepChess obtaining a grandmaster-level chess playing performance using a learning method incorporating two deep neural networks, which are trained using a combination of unsupervised pretraining and supervised training. The unsupervised training extracts high level features from a given chess position, and the supervised training learns to compare two chess positions to select the more favorable one. In order to use DeepChess inside a chess program, a novel version of alpha-beta is used that does not require bounds but positions αpos and βpos [13].

AlphaZero

In December 2017, the Google DeepMind team with Matthew Lai involved published on their generalized AlphaZero algorithm, combining 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 [14].

Leela Chess Zero

Leela Chess Zero is an adaptation of Gian-Carlo Pascutto's Leela Zero Go project [15] to Chess.

See also

Selected Publications

1965 ...

1980 ...

1990 ...

2000 ...

2010 ...

2013

2014

2015 ...

2016

2017

2018

2019

2020 ...

2021

Forum Posts

2014

2015 ...

2016

Re: Deep Learning Chess Engine ? by Alexandru Mosoi, CCC, July 21, 2016 » Zurichess
Re: Deep Learning Chess Engine ? by Matthew Lai, CCC, August 04, 2016 » Giraffe [52]

2017

Re: Is AlphaGo approach unsuitable to chess? by Peter Österlund, CCC, May 31, 2017 » Texel
Re: To TPU or not to TPU... by Rémi Coulom, CCC, December 16, 2017

2018

2019

Re: A question to MCTS + NN experts by Daniel Shawul, CCC, July 17, 2019

2020 ...

External Links

Networks

Convolutional Neural Networks for Image and Video Processing, TUM Wiki, Technical University of Munich
An Introduction to different Types of Convolutions in Deep Learning by Paul-Louis Pröve, July 22, 2017
Squeeze-and-Excitation Networks by Paul-Louis Pröve, October 17, 2017

Software

Libraries

Chess

Games

Music Generation

Nvidia

Reports & Blogs

Texas Hold'em: AI is almost as good as humans at playing poker by Matt Burgess, Wired UK, March 30, 2016
GitHub - suragnair/alpha-zero-general: A clean and simple implementation of a self-play learning algorithm based on AlphaGo Zero (any game, any framework!)

Videos

References

  1. Image based on HDLTex: Hierarchical Deep Learning for Text Classification by Kk7nc, December 14, 2017, Hierarchical Deep Learning from Wikipedia
  2. PARsE | Education | GPU Cluster | Efficient mapping of the training of Convolutional Neural Networks to a CUDA-based cluster
  3. Christopher Clark, Amos Storkey (2014). Teaching Deep Convolutional Neural Networks to Play Go. arXiv:1412.3409
  4. Teaching Deep Convolutional Neural Networks to Play Go by Hiroshi Yamashita, The Computer-go Archives, December 14, 2014
  5. Why Neural Networks Look Set to Thrash the Best Human Go Players for the First Time | MIT Technology Review, December 15, 2014
  6. Teaching Deep Convolutional Neural Networks to Play Go by Michel Van den Bergh, CCC, December 16, 2014
  7. Chris J. Maddison, Aja Huang, Ilya Sutskever, David Silver (2014). Move Evaluation in Go Using Deep Convolutional Neural Networks. arXiv:1412.6564v1
  8. Move Evaluation in Go Using Deep Convolutional Neural Networks by Aja Huang, The Computer-go Archives, December 19, 2014
  9. AlphaGo | Google DeepMind
  10. David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, Demis Hassabis (2016). Mastering the game of Go with deep neural networks and tree search. Nature, Vol. 529
  11. *First release* Giraffe, a new engine based on deep learning by Matthew Lai, CCC, July 08, 2015
  12. Re: Deep Learning Chess Engine ? by Alexandru Mosoi, CCC, July 21, 2016
  13. Omid E. David, Nathan S. Netanyahu, Lior Wolf (2016). DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess. ICAAN 2016, Lecture Notes in Computer Science, Vol. 9887, Springer, pdf preprint
  14. 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. arXiv:1712.01815
  15. [- gcp/leela-zero: Go engine with no human-provided knowledge, modeled after the AlphaGo Zero paper]
  16. Neocognitron - Scholarpedia by Kunihiko Fukushima
  17. Who introduced the term “deep learning” to the field of Machine Learning by Jürgen Schmidhuber, Google+, March 18, 2015
  18. Sepp Hochreiter's Fundamental Deep Learning Problem (1991) by Jürgen Schmidhuber, 2013
  19. Long short term memory from Wikipedia
  20. Who introduced the term “deep learning” to the field of Machine Learning by Jürgen Schmidhuber, Google+, March 18, 2015
  21. Demystifying Deep Reinforcement Learning by Tambet Matiisen, Nervana, December 21, 2015
  22. high dimensional optimization by Warren D. Smith, FishCooking, December 27, 2019
  23. Teaching Deep Convolutional Neural Networks to Play Go by Hiroshi Yamashita, The Computer-go Archives, December 14, 2014
  24. Teaching Deep Convolutional Neural Networks to Play Go by Michel Van den Bergh, CCC, December 16, 2014
  25. Re: To TPU or not to TPU... by Rémi Coulom, CCC, December 16, 2017
  26. How Facebook’s AI Researchers Built a Game-Changing Go Engine | MIT Technology Review, December 04, 2015
  27. Combining Neural Networks and Search techniques (GO) by Michael Babigian, CCC, December 08, 2015
  28. Quoc Le’s Lectures on Deep Learning | Gaurav Trivedi
  29. GitHub - BarakOshri/ConvChess: Predicting Moves in Chess Using Convolutional Neural Networks
  30. ConvChess CNN by Brian Richardson, CCC, March 15, 2017
  31. Jürgen Schmidhuber (2015) Critique of Paper by "Deep Learning Conspiracy" (Nature 521 p 436).
  32. DeepChess: Another deep-learning based chess program by Matthew Lai, CCC, October 17, 2016
  33. ICANN 2016 | Recipients of the best paper awards
  34. Jigsaw puzzle from Wikipedia
  35. Could DeepMind try to conquer poker next? by Alex Hern, The Guardian, March 30, 2016
  36. CMA-ES from Wikipedia
  37. catastrophic forgetting by Daniel Shawul, CCC, May 09, 2019
  38. Stockfish NN release (NNUE) by Henk Drost, CCC, May 31, 2020 » Stockfish
  39. AlphaGo Zero: Learning from scratch by Demis Hassabis and David Silver, DeepMind, October 18, 2017
  40. GitHub - suragnair/alpha-zero-general: A clean and simple implementation of a self-play learning algorithm based on AlphaGo Zero (any game, any framework!)
  41. GitHub - mil-tokyo/webdnn: The Fastest DNN Running Framework on Web Browser
  42. GitHub - paintception/DeepChess
  43. Edax by Richard Delorme
  44. Deep Pepper Paper by Leo, CCC, July 07, 2018
  45. AlphaZero: Shedding new light on the grand games of chess, shogi and Go by David Silver, Thomas Hubert, Julian Schrittwieser and Demis Hassabis, DeepMind, December 03, 2018
  46. MuZero: Mastering Go, chess, shogi and Atari without rules
  47. GitHub - koulanurag/muzero-pytorch: Pytorch Implementation of MuZero
  48. Book about Neural Networks for Chess by dkl, CCC, September 29, 2021
  49. Acquisition of Chess Knowledge in AlphaZero, ChessBase News, November 18, 2021
  50. Rina Dechter (1986). Learning While Searching in Constraint-Satisfaction-Problems. AAAI 86, pdf
  51. GitHub - pluskid/Mocha.jl: Deep Learning framework for Julia
  52. Rectifier (neural networks) from Wikipedia
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  54. Barak Oshri, Nishith Khandwala (2015). Predicting Moves in Chess using Convolutional Neural Networks. pdf
  55. Re: Google's AlphaGo team has been working on chess by Brian Richardson, CCC, December 09, 2017
  56. Connect 4 AlphaZero implemented using Python... by Steve Maughan, CCC, January 29, 2018
  57. Basic Linear Algebra Subprograms - Functionality - Level 3 | Wikipedia
  58. Re: To TPU or not to TPU... by Rémi Coulom, CCC, December 16, 2017
  59. Yuandong Tian, Yan Zhu (2015). Better Computer Go Player with Neural Network and Long-term Prediction. arXiv:1511.06410
  60. Johannes Heinrich, David Silver (2016). Deep Reinforcement Learning from Self-Play in Imperfect-Information Games. arXiv:1603.01121
  61. A Simple Alpha(Go) Zero Tutorial by Oliver Roese, CCC, December 30, 2017

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