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cs.LG2025
Neural Logic Networks for Interpretable Classification
Vincent Perreault, Katsumi Inoue, Richard Labib +1
Traditional neural networks have an impressive classification performance, but what they learn cannot be inspected, verified or extracted. Neural Logic Networks on the other hand h…
cs.LG2025
Disentangling Neural Disjunctive Normal Form Models
Kexin Gu Baugh, Vincent Perreault, Matthew Baugh +3
Neural Disjunctive Normal Form (DNF) based models are powerful and interpretable approaches to neuro-symbolic learning and have shown promising results in classification and reinfo…
cs.LG2024
Variable Assignment Invariant Neural Networks for Learning Logic Programs
Yin Jun Phua, Katsumi Inoue
Learning from interpretation transition (LFIT) is a framework for learning rules from observed state transitions. LFIT has been implemented in purely symbolic algorithms, but they…