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: 0.6850 | Eval Acc: 0.6500 Val Coverage: (N_b=49, N_r=47, N_r/N_b=0.9592) Train Coverage: (N_b=225, N_r=223, N_r/N_b=0.9911)
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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.