2 papers
cs.LG2026
TT-Sparse: Learning Sparse Rule Models with Differentiable Truth Tables
Hans Farrell Soegeng, Sarthak Ketanbhai Modi, Thomas Peyrin
Interpretable machine learning is essential in high-stakes domains where decision-making requires accountability, transparency, and trust. While rule-based models offer global and…
cs.AI2023
Neural Network-Based Rule Models With Truth Tables
Adrien Benamira, Tristan Guérand, Thomas Peyrin +1
Understanding the decision-making process of a machine/deep learning model is crucial, particularly in security-sensitive applications. In this study, we introduce a neural network…