Differentiable Rule Induction from Raw Sequence Inputs
Institute of High Performance Computing, Agency for Science, Technology and Research（高性能计算研究所，科技研究局）；National Institute of Informatics（国家信息研究所）；Key Laboratory of High Confidence Software Technologies, School of Computer Science Peking University（高可信软件技术重点实验室，北京大学计算机学院）
Kun Gao, Katsumi Inoue, Yongzhi Cao, Hanpin Wang, Feng Yang
Rule learning-based models are widely used in highly interpretable scenarios due to their transparent structures. Inductive logic programming (ILP), a form of machine learning, induces rules from facts while maintaining interpretability. Differentiable ILP models enhance this process by leveraging neural networks to improve robustness and scalability. However, most differentiable ILP methods rely on symbolic datasets, facing challenges when learning directly from raw data. Specifically, they struggle with explicit label leakage: The inability to map continuous inputs to symbolic variables without explicit supervision of input feature labels. In this work, we address this issue by integrating a self-supervised differentiable clustering model with a novel differentiable ILP model, enabling rule learning from raw data without explicit label leakage. The learned rules effectively describe raw data through its features. We demonstrate that our method intuitively and precisely learns generalized rules from time series and image data.
SCOPE and SCION: A Benchmark and an Auditable Reference Pipeline for Schema Induction and Fusion from Text
Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China（信息工程研究所，中国科学院，北京，中国）；School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China（网络安全学院，中国科学院大学，北京，中国）；School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China（人工智能学院，中国科学院大学，北京，中国）
ANDRE: An Attention-based Neuro-symbolic Differentiable Rule Extractor for Inductive Logic Programming
Dept. of Mechanical and Aerospace Engineering, George Washington University, USA（机械与航空航天工程系，乔治华盛顿大学）；Dept. of Mechanical Engineering Sciences, University of Surrey, UK（机械工程科学系，萨里大学）
