1 citations · 1 across the 4 of their papers we have counts for
5 papers
Constraint-Based Analysis of Reasoning Shortcuts in Neurosymbolic Learning
Akihiro Takemura, Katsumi Inoue, Masaaki Nishino
Neurosymbolic systems can satisfy logical constraints during learning without achieving the intended concept-label correspondence; this is a problem known as reasoning shortcuts. W…
Visual Perceptual to Conceptual First-Order Rule Learning Networks
Kun Gao, Davide Soldà, Thomas Eiter +1
Learning rules plays a crucial role in deep learning, particularly in explainable artificial intelligence and enhancing the reasoning capabilities of large language models. While e…
Formally Explaining Decision Tree Models with Answer Set Programming
Akihiro Takemura, Masayuki Otani, Katsumi Inoue
Decision tree models, including random forests and gradient-boosted decision trees, are widely used in machine learning due to their high predictive performance. However, their com…
Generating Global and Local Explanations for Tree-Ensemble Learning Methods by Answer Set Programming
Akihiro Takemura, Katsumi Inoue
We propose a method for generating rule sets as global and local explanations for tree-ensemble learning methods using Answer Set Programming (ASP). To this end, we adopt a decompo…
Differentiable Logic Programming for Distant Supervision
Akihiro Takemura, Katsumi Inoue
We introduce a new method for integrating neural networks with logic programming in Neural-Symbolic AI (NeSy), aimed at learning with distant supervision, in which direct labels ar…