8 citations · 12 across the 5 of their papers we have counts for
5 papers
NSFL: A Post-Training Neuro-Symbolic Fuzzy Logic Framework for Boolean Operators in Neural Embeddings
Vladi Vexler, Ofer Idan, Gil Lederman +1
Standard dense retrievers lack a native calculus for multi-atom logical constraints. We introduce Neuro-Symbolic Fuzzy Logic (NSFL), a framework that adapts formal t-norms and t-co…
Rotation Invariant Quantization for Model Compression
Joseph Kampeas, Yury Nahshan, Hanoch Kremer +4
Post-training Neural Network (NN) model compression is an attractive approach for deploying large, memory-consuming models on devices with limited memory resources. In this study,…
Demonstration Informed Specification Search
Marcell Vazquez-Chanlatte, Ameesh Shah, Gil Lederman +1
This paper considers the problem of learning temporal task specifications, e.g. automata and temporal logic, from expert demonstrations. Task specifications are a class of sparse m…
Learning Branching Heuristics for Propositional Model Counting
Pashootan Vaezipoor, Gil Lederman, Yuhuai Wu +4
Propositional model counting, or #SAT, is the problem of computing the number of satisfying assignments of a Boolean formula. Many problems from different application areas, includ…
Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement Learning
Gil Lederman, Markus N. Rabe, Edward A. Lee +1
We demonstrate how to learn efficient heuristics for automated reasoning algorithms for quantified Boolean formulas through deep reinforcement learning. We focus on a backtracking…