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By introducing several improvements to the AlphaZero process and architecture, we greatly accelerate self-play learning in Go, achieving a 50x reduction in computation over comparable methods.
Francesco Morandin, Gianluca Amato, Marco Fantozzi, Rosa Gini, Carlo Metta, and Maurizio Parton · 1905
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Life in the game of go
David Benson · 1976
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Bayesian elo rating, 2010
Remi Coulom · 2010
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The game of go, 2014
John Tromp · 2014
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Training deep convolutional neural networks to play go
Christopher Clark and Amos Storkey · 2015
Earlier work this paper cites.
Move evaluation in go using deep convolutional neural networks
Chris Maddison, Aja Huang, Ilya Sutskever, and David Silver · 2015
Earlier work this paper cites.
Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Better computer go player with neural network and long-term prediction
Yuandong Tian and Yan Zhu · 2016
Cited alongside, same era.
Identifying beneficial task relations for multi-task learning in deep neural networks
Joachim Bingel and Anders Søgaard · 2017
Cited alongside, same era.
Residual networks for computer go
Tristan Cazenave · 2017
Cited alongside, same era.
Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
Cited alongside, same era.
A survey on multi-task learning, 2017
Yu Zhang and Qiang Yang · 2017
Cited alongside, same era.
Squeeze-and-excitation networks
Jie Hu, Li Shen, Samuel Albanie, Gang Sun, and Enhua Wu · 2018
Multi-labelled value networks for computer go
Ti-Rong Wu, I-Chen Wu, Guan-Wun Chen, Ting han Wei, Tung-Yi Lai, Hung-Chun Wu, and Li-Cheng Lan · 2018
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Optimal amount of visits per move, 2019
Henrik Forsten · 2019
Closest in time.
Leela Chess Zero project main webpage, https://lczero.org/
Gary Linscott et al., 2019 · 2019
Closest in time.
MiniGo project main GitHub page, https://github.com/tensorflow/minigo/
Tom Madams, Andrew Jackson, et al., 2019 · 2019
Closest in time.
Leela Zero project main webpage, https://zero.sjeng.org/
Gian-Carlo Pascutto et al., 2019 · 2019
Closest in time.
Fresh max_lcb_root experiments, 2019
Jonathan Roy · 2019
Closest in time.
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Cited alongside, same era.
Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson · 2018
Cited alongside, same era.
A general reinforcement learning algorithm that masters chess, shogi, and go through selfplay
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2018
Cited alongside, same era.
Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, and C. Lawrence Zitnick · 2019
Closest in time.