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Experimental results show that the proposed hyperbolic manifold constrained tabular neural network is effective, and HTNN ranks first among 36 methods on 200 classification datasets and third among 34 methods on 100 regression datasets.
RGRS is proposed, a unified neuro-symbolic framework that integrates rule-level reasoning with graph representation learning for scalable Horn rule instantiation and achieves consistently high accuracy with significant efficiency gains over symbolic and neuro-symbolic baselines.
RGRS is proposed, a unified neuro-symbolic framework that integrates rule-level reasoning with graph representation learning for scalable Horn rule instantiation and achieves consistently high accuracy with significant efficiency gains over symbolic and neuro-symbolic baselines.
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