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Playing board games is considered a major challenge for both humans and AI researchers.
The game of go: the national game of Japan
Arthur Smith · 1908
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Othello: Brief & basic
Ted Landau · 1985
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An artificial neural network for spatio-temporal bipolar patterns: Application to phoneme classification
Les Atlas, Toshiteru Homma, and Robert Marks · 1987
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Principal component analysis
Svante Wold, Kim Esbensen, and Paul Geladi · 1987
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Efficient selectivity and backup operators in monte-carlo tree search
Rémi Coulom · 2006
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A scalable machine learning approach to go
Lin Wu and Pierre Baldi · 2007
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Scalable neural networks for board games
Tom Schaul and Jürgen Schmidhuber · 2009
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Indirect encoding of neural networks for scalable go
Jason Gauci and Kenneth O Stanley · 2010
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
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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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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Discriminative embeddings of latent variable models for structured data
Hanjun Dai, Bo Dai, and Le Song · 2016
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Learning combinatorial optimization algorithms over graphs
Hanjun Dai, Elias B Khalil, Yuyu Zhang, Bistra Dilkina, and Le Song · 2017
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Graph convolutional matrix completion
Rianne van den Berg, Thomas N Kipf, and Max Welling · 2017
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Learning heuristics for quantified boolean formulas through deep reinforcement learning
Gil Lederman, Markus N Rabe, Edward A Lee, and Sanjit A Seshia · 2018
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Solving np-hard problems on graphs by reinforcement learning without domain knowledge
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Inductive representation learning on large graphs
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Mastering chess and shogi by self-play with a general reinforcement learning algorithm
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al
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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
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Kenshin Abe, Zijian Xu, Issei Sato, and Masashi Sugiyama · 2019
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Learning local search heuristics for boolean satisfiability
Emre Yolcu and Barnabás Póczos · 2019
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Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
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A graph neural network assisted monte carlo tree search approach to traveling salesman problem
Zhihao Xing and Shikui Tu · 2020
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