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Hessian rank

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papersTODAY 04:00 UTC

arXiv Paper Examines Symmetries and Singularities in Over-Parameterized Neural Networks

A new arXiv preprint argues that parameter counts and Hessian rank are insufficient for measuring the effective complexity of deep neural networks, since many different parameter settings produce identical predictions. The work analyzes the symmetries and singular structure of the loss landscape to better characterize model complexity. It appears in the cs.LG category as a new submission.

deep-learning/arxiv/theory/over-parameterization