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This paper shows that cross-entropy and supervised contrastive learning are both forms of prototype learning on the hypersphere and proposes normalized losses (NTCE and NONL) that achieve Neural Collapse by design, outperforming standard methods.
This paper derives a closed-form upper bound for admissible learning-rate steps in belief-space dynamics using KL divergence and Bregman geometry, focusing on cross-entropy classification.