Fetching the paper…
Reading the bibliography…
We use neural graph networks with a message-passing architecture and an attention mechanism to enhance the branching heuristic in two SAT-solving algorithms.
Reducibility among combinatorial problems
R. Karp · 1972
Earlier work this paper cites.
The impact of branching heuristics in propositional satisfiability algorithms
Joao Marques-Silva · 1999
Earlier work this paper cites.
Chaff: Engineering an efficient SAT solver
Matthew W. Moskewicz, Conor F. Madigan, Ying Zhao, Lintao Zhang, and Sharad Malik · 2001
Earlier work this paper cites.
An extensible sat-solver
Niklas Eén and Niklas Sörensson · 2003
Earlier work this paper cites.
Simple SAT solver with CDCL implemented in Python
Zhang Zhongwei · 2005
Earlier work this paper cites.
Rectifier nonlinearities improve neural network acoustic models
Andrew L. Maas, Awni Y. Hannun, and Andrew Y. Ng · 2013
Earlier work this paper cites.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
Cited alongside, same era.
Learning continuous semantic representations of symbolic expressions
Miltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli, and Charles A. Sutton · 2016
Cited alongside, same era.
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2017
Cited alongside, same era.
Premise selection for theorem proving by deep graph embedding
Mingzhe Wang, Yihe Tang, Jian Wang, and Jia Deng · 2017
PySAT: A Python toolkit for prototyping with SAT oracles
Alexey Ignatiev, Antonio Morgado, and Joao Marques-Silva · 2018
Later among the works it cites.
Reinforcement learning of theorem proving
Cezary Kaliszyk, Josef Urban, Henryk Michalewski, and Mirek Olsák · 2018
Later among the works it cites.
Machine learning-based restart policy for CDCL SAT solvers
Jia Hui Liang, Chanseok Oh, Minu Mathew, Ciza Thomas, Chunxiao Li, and Vijay Ganesh · 2018
Later among the works it cites.
Learning a SAT solver from single-bit supervision
Daniel Selsam, Matthew Lamm, Benedikt Bünz, Percy Liang, Leonardo de Moura, and David L. Dill · 2018
Later among the works it cites.
Automated proof synthesis for propositional logic with deep neural networks
Taro Sekiyama and Kohei Suenaga · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinícius Flores Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Çaglar Gülçehre, Francis Song, Andrew J. Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matthew Botvinick, Oriol Vinyals, Yujia Li, and Razvan Pascanu · 2018
Cited alongside, same era.
Can neural networks understand logical entailment?
Richard Evans, David Saxton, David Amos, Pushmeet Kohli, and Edward Grefenstette · 2018
Cited alongside, same era.
Later among the works it cites.
Learning clause deletion heuristics with reinforcement learning
Pashootan Vaezipoor, Gil Lederman, Yuhuai Wu, Roger Grosse, and Fahiem Bacchus · 2020
Closest in time.