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.
29 Training Acc: 0.9474 | Eval Acc: 0.9474 Val Coverage: (N_b=486, N_r=455, N_r/N_b=0.9362) Train Coverage: (N_b=9019, N_r=8383, N_r/N_b=0.9295)
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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.