ANDRE learns first-order logic programs via attention-driven differentiable operators that approximate logical semantics, achieving competitive performance and rule recovery on probabilistic ILP benchmarks.
Training Acc: 0.8695 | Eval Acc: 0.8817 Val Coverage: (N_b=111, N_r=100, N_r/N_b=0.9009) Train Coverage: (N_b=2130, N_r=1860, N_r/N_b=0.8732)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
ANDRE: An Attention-based Neuro-symbolic Differentiable Rule Extractor for Inductive Logic Programming
ANDRE learns first-order logic programs via attention-driven differentiable operators that approximate logical semantics, achieving competitive performance and rule recovery on probabilistic ILP benchmarks.