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Found 2 papers, 1 papers with code
Learning a SAT Solver from Single-Bit Supervision
We present NeuroSAT, a message passing neural network that learns to solve SAT problems after only being trained as a classifier to predict satisfiability.
Graph Neural Networks and Boolean Satisfiability
In a weakly-supervised setting, that is, without problem specific feature engineering, Graph Neural Networks can learn features of satisfiability.
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