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* [[Tristan Cazenave]] ('''2017'''). ''[http://ieeexplore.ieee.org/document/7875402/ Residual Networks for Computer Go]''. [[IEEE#TOCIAIGAMES|IEEE Transactions on Computational Intelligence and AI in Games]], Vol. PP, No. 99, [http://www.lamsade.dauphine.fr/~cazenave/papers/resnet.pdf pdf]
* [[Shi-Jim Yen]], [[Ching-Nung Lin]], [[Guan-Lun Cheng]], [[Jr-Chang Chen]] ('''2017'''). ''[http://ieeexplore.ieee.org/document/7966187/ Deep Learning and Block Go]''. [http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7958416 IJCNN 2017]
* [[Matej Moravčík]], [[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]
* [[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]
* [[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]
* [[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]
* [[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]
* [[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]
* [[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]
* [[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]], [[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]
* [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'''
* [[Paweł Liskowski]], [[Wojciech Jaśkowski]], [[Krzysztof Krawiec]] ('''2018'''). ''Learning to Play Othello with Deep Neural Networks''. [[IEEE#TOG|IEEE Transactions on Games]]
