Corpus ID: 102354917

Learning to Reason: Leveraging Neural Networks for Approximate DNF Counting

@article{Abboud2019LearningTR,
  title={Learning to Reason: Leveraging Neural Networks for Approximate DNF Counting},
  author={Ralph Abboud and Ismail Ilkan Ceylan and Thomas Lukasiewicz},
  journal={ArXiv},
  year={2019},
  volume={abs/1904.02688}
}
  • Ralph Abboud, Ismail Ilkan Ceylan, Thomas Lukasiewicz
  • Published 2019
  • Computer Science
  • ArXiv
  • Weighted model counting (WMC) has emerged as a prevalent approach for probabilistic inference. In its most general form, WMC is #P-hard. Weighted DNF counting (weighted #DNF) is a special case, where approximations with probabilistic guarantees are obtained in O(nm), where n denotes the number of variables, and m the number of clauses of the input DNF, but this is not scalable in practice. In this paper, we propose a neural model counting approach for weighted #DNF that combines approximate… CONTINUE READING
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