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.LG2026
Navigating the Deep: End-to-End Extraction on Deep Neural Networks
Haolin Liu, Adrien Siproudhis, Samuel Experton +3
Neural network model extraction has recently emerged as an important security concern, as adversaries attempt to recover a network's parameters via black-box queries. Carlini et al…