A differentiable first-order rule learner for inductive logic programming

DOI10.1016/J.ARTINT.2024.104108MaRDI QIDQ6566626

Yongzhi Cao, Kun Gao, Katsumi Inoue, Hanpin Wang

Publication date: 3 July 2024

Published in: Artificial Intelligence (Search for Journal in Brave)

zbMATH Keywords

interpretabilitymachine learningdifferentiable inductive logic programmingneuro-symbolic method

Mathematics Subject Classification ID

Artificial intelligence (68Txx)

Cites Work

• Title not available (Why is that?)
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• ILP turns 20. Biography and future challenges
• Clausal discovery
• Logic programming in tensor spaces
• Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited
• Metamathematics of fuzzy logic
• Learning from interpretation transition using differentiable logic programming semantics
• Inductive logic programming at 30
• Making sense of sensory input
• Making sense of raw input
• Learning from interpretation transition
• Gradient-based supported model computation in vector spaces
• Inductive Learning of Answer Set Programs
• Ontology Reasoning with Deep Neural Networks
• Exploiting Answer Set Programming with External Sources for Meta-Interpretive Learning
• Learning Explanatory Rules from Noisy Data
• Inductive logic programming
• Symbolic knowledge extraction from trained neural networks: A sound approach

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