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Enhancing SAT solvers with glue variable predictions

Enhancing SAT solvers with glue variable predictions

6 July 2020
Jesse Michael Han
    NAIAAML
ArXiv (abs)PDFHTML

Papers citing "Enhancing SAT solvers with glue variable predictions"

9 / 9 papers shown
AutoSAT: Automatically Optimize SAT Solvers via Large Language Models
AutoSAT: Automatically Optimize SAT Solvers via Large Language Models
Yiwen Sun
Xianyin Zhang
Shiyu Huang
Shaowei Cai
Bing-Zhen Zhang
Ke Wei
234
12
0
16 Feb 2024
G4SATBench: Benchmarking and Advancing SAT Solving with Graph Neural
  Networks
G4SATBench: Benchmarking and Advancing SAT Solving with Graph Neural Networks
Zhaoyu Li
Jinpei Guo
Xujie Si
NAI
322
16
0
29 Sep 2023
Using deep learning to construct stochastic local search SAT solvers with performance bounds
Using deep learning to construct stochastic local search SAT solvers with performance bounds
Maximilian Kramer
Paul Boes
Jens Eisert
NAILRM
311
0
0
20 Sep 2023
Machine Learning for SAT: Restricted Heuristics and New Graph
  Representations
Machine Learning for SAT: Restricted Heuristics and New Graph Representations
Mikhail Shirokikh
Ilya Shenbin
Anton M. Alekseev
Sergey I. Nikolenko
NAI
196
1
0
18 Jul 2023
HardSATGEN: Understanding the Difficulty of Hard SAT Formula Generation
  and A Strong Structure-Hardness-Aware Baseline
HardSATGEN: Understanding the Difficulty of Hard SAT Formula Generation and A Strong Structure-Hardness-Aware BaselineKnowledge Discovery and Data Mining (KDD), 2023
Yongqian Li
Xinyan Chen
Wenxuan Guo
Xijun Li
Wanqian Luo
Jun Huang
Hui-Ling Zhen
Mingxuan Yuan
Junchi Yan
497
23
0
04 Feb 2023
Denoising Diffusion for Sampling SAT Solutions
Denoising Diffusion for Sampling SAT Solutions
Kārlis Freivalds
Sergejs Kozlovics
248
5
0
30 Nov 2022
Machine Learning Methods in Solving the Boolean Satisfiability Problem
Machine Learning Methods in Solving the Boolean Satisfiability ProblemMachine Intelligence Research (MIR), 2022
Wenxuan Guo
Junchi Yan
Hui-Ling Zhen
Xijun Li
Mingxuan Yuan
Yaohui Jin
NAI
305
50
0
02 Mar 2022
NeuroBack: Improving CDCL SAT Solving using Graph Neural Networks
NeuroBack: Improving CDCL SAT Solving using Graph Neural Networks
Wenxi Wang
Yang Hu
Mohit Tiwari
S. Khurshid
K. McMillan
Risto Miikkulainen
GNNNAI
420
23
0
26 Oct 2021
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning
LIME: Learning Inductive Bias for Primitives of Mathematical ReasoningInternational Conference on Machine Learning (ICML), 2021
Yuhuai Wu
M. Rabe
Wenda Li
Jimmy Ba
Roger C. Grosse
Christian Szegedy
AIMatLRM
365
66
0
15 Jan 2021
1
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