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Found 5 papers, 3 papers with code
A Survey on Deep Learning for Theorem Proving
Theorem proving is a fundamental aspect of mathematics, spanning from informal reasoning in mathematical language to rigorous derivations in formal systems.
G4SATBench: Benchmarking and Advancing SAT Solving with Graph Neural Networks
Graph neural networks (GNNs) have recently emerged as a promising approach for solving the Boolean Satisfiability Problem (SAT), offering potential alternatives to traditional backtracking or local search SAT solvers.
NSNet: A General Neural Probabilistic Framework for Satisfiability Problems
We present the Neural Satisfiability Network (NSNet), a general neural framework that models satisfiability problems as probabilistic inference and meanwhile exhibits proper explainability.
Multimodal Emotion-Cause Pair Extraction in Conversations
It is also interesting to discover emotions and their causes in conversations.
Graph Contrastive Pre-training for Effective Theorem Reasoning
Interactive theorem proving is a challenging and tedious process, which requires non-trivial expertise and detailed low-level instructions (or tactics) from human experts.
