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: 0.8625 | Eval Acc: 0.8568 Val Coverage: (N_b=290, N_r=270, N_r/N_b=0.9310) Train Coverage: (N_b=5394, N_r=5022, N_r/N_b=0.9310)
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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.