Maedeh Zarvandi

Interpretable Self-Supervised Learning via Representer Landmarks and Nyström Approximation

Self-supervised learning (SSL) effectively learns representations from massive unlabeled data, yet the resulting models typically operate as black boxes, necessitating domain-specific post-hoc explanations. We introduce KREPES, a unified framework that learns inherently interpretable representations…