Learning Better Representations From Less Data For Propositional Satisfiability

Training neural networks on NP-complete problems typically demands very large amounts of training data and often needs to be coupled with computationally expensive symbolic verifiers to ensure output correctness. In this paper, we present NeuRes, a neuro-symbolic approach to address both challenges...

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Veröffentlicht in:	arXiv.org 2024-11
Hauptverfasser:	Ghanem, Mohamed, Schmitt, Frederik, Siber, Julian, Finkbeiner, Bernd
Format:	Artikel
Sprache:	eng
Schlagworte:	Datasets Graph neural networks Solvers Teachers
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