The Loss Is Not Enough: Sampling Conditions and Inductive Bias in Contrastive Representation Learning 事件

The Loss Is Not Enough: Sampling Conditions and Inductive Bias in Contrastive Representation Learning arXiv:2606.04280v1 Announce Type: cross Abstract: Contrastive learning has become a leading paradigm for self-supervised representation learning, yet the conditions under which it recovers meaningful latent geometry remain incompletely understood. We develop a measure-theoretic framework formalizing the diversity condition, a support requirement on positive-pair sampling that is necessary for i