3 papers
cs.LG2026
Geometric Reasoning in the Embedding Space
Jan Hůla, David MojžÃÅ¡ek, JiÅà JaneÄek +2
In this contribution, we demonstrate that Graph Neural Networks and Transformers can learn to reason about geometric constraints. We train them to predict spatial position of point…
cs.LG2025
Neural Approaches to SAT Solving: Design Choices and Interpretability
David MojžÃÅ¡ek, Jan Hůla, Ziwei Li +2
In this contribution, we provide a comprehensive evaluation of graph neural networks applied to Boolean satisfiability problems, accompanied by an intuitive explanation of the mech…
cs.LG2024
Understanding GNNs for Boolean Satisfiability through Approximation Algorithms
Jan Hůla, David MojžÃÅ¡ek, Mikoláš Janota
The paper deals with the interpretability of Graph Neural Networks in the context of Boolean Satisfiability. The goal is to demystify the internal workings of these models and prov…