AI RESEARCH
Interpretable Self-Supervised Learning via Representer Landmarks and Nystr\"om Approximation
arXiv CS.LG
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ArXi:2509.24467v3 Announce Type: replace Self-supervised learning (SSL) learns representations from massive unlabeled data, yet the resulting models typically operate as black boxes, necessitating domain-specific explanations. We