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* [[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
* [[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]]
* [[Arvind Neelakantan]], [[Luke Vilnis]], [[Quoc V. Le]], [[Ilya Sutskever]], [[Lukasz Kaiser]], [[Karol Kurach]], [[James Martens]] ('''2015'''). ''Adding Gradient Noise Improves Learning for Very Deep Networks''. [https://wwwarxiv.linkedin.comorg/inabs/quoc-v-le-319b5a8 1511.06807 arXiv:1511.06807]* [[Quoc V. Le]] ('''2015'''). ''A Tutorial on Deep Learning - Part 1: Nonlinear Classifiers and The Backpropagation Algorithm''. [[Google#Brain|Google Brain]], [https://cs.stanford.edu/~quocle/tutorial1.pdf pdf] <ref>[http://www.trivedigaurav.com/blog/quoc-les-lectures-on-deep-learning/ Quoc Le’s Lectures on Deep Learning | Gaurav Trivedi]</ref>* [https://www.linkedin.com/in/quoc-v-le-319b5a8 [Quoc V. Le]] ('''2015'''). ''A Tutorial on Deep Learning - Part 2: Autoencoders, Convolutional Neural Networks and Recurrent Neural Networks''. [[Google#Brain|Google Brain]], [http://ai.stanford.edu/~quocle/tutorial2.pdf pdf]
* [[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]
* [[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]
* [[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]
* [[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]
* [[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]
* [[Sergey Ioffe]] ('''2017'''). ''Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models''. [https://arxiv.org/abs/1702.03275 arXiv:1702.03275]
* [[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]
* [[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
* [[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>
* [[Naman Agarwal]], [[Brian Bullins]], [[Xinyi Chen]], [[Elad Hazan]], [[Karan Singh]], [[Cyril Zhang]], [[Yi Zhang]] ('''2018'''). ''The Case for Full-Matrix Adaptive Regularization''. [https://arxiv.org/abs/1806.02958 arXiv:1806.02958]
* [[Ashwin Srinivasan]], [[Lovekesh Vig]], [[Michael Bain]] ('''2018'''). ''Logical Explanations for Deep Relational Machines Using Relevance Information''. [https://arxiv.org/abs/1807.00595 arXiv:1807.00595]
* [[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]] ('''2018'''). ''[http://science.sciencemag.org/content/362/6419/1140 A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play]''. [https://en.wikipedia.org/wiki/Science_(journal) Science], Vol. 362, No. 6419 <ref>[https://deepmind.com/blog/alphazero-shedding-new-light-grand-games-chess-shogi-and-go/ 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</ref>
