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* [[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]], [[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]
* [[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]
* [[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]
* [[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>
* [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]
'''2018'''
* [[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]
* [[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>
* [[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]
