softmax-codes

This paper studies how depth alone induces an implicit low-rank bias in deep unconstrained feature models trained without regularization, shifting the optimal solution from neural collapse to softmax codes, and provides the first asymptotic and dynamic characterization of this bias under gradient descent with cross-entropy loss.