GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection-Reference-Cited by-同舟云学术

GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection

Published:2024-08-24 Issue: Volume:24 Page:6301-6311
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Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Zhang Zhanguang¹^ORCID,Chételat Didier¹^ORCID,Cotnareanu Joseph²^ORCID,Ghose Amur¹^ORCID,Xiao Wenyi³^ORCID,Zhen Hui-Ling³^ORCID,Zhang Yingxue¹^ORCID,Hao Jianye⁴^ORCID,Coates Mark²^ORCID,Yuan Mingxuan³^ORCID

Affiliation:

1. Huawei Noah's Ark Lab, Montreal, Canada

2. McGill University, Montreal, Canada

3. Huawei Noah's Ark Lab, Hong Kong, China

4. Huawei Noah's Ark Lab, Beijing, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671627

Reference50 articles.

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3. Armin Biere and Mathias Fleury. 2022. Gimsatul, IsaSAT and Kissat entering the SAT Competition 2022. In Proc. of SAT Competition 2022 - Solver and Benchmark Descriptions (Department of Computer Science Series of Publications B, Vol. B-2022-1), Tomas Balyo, Marijn Heule, Markus Iser, Matti Järvisalo, and Martin Suda (Eds.). University of Helsinki, 10--11.

4. Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, and Joaquin Vanschoren. 2016. ASlib: A benchmark library for algorithm selection. Artificial Intelligence (2016).

5. Combinatorial optimization and reasoning with graph neural networks;Cappart Quentin;Journal of Machine Learning Research,2023