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model complexity

topic2 events
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
papersSEP 11 04:00 UTC

Study Links Zero Pattern of Design Matrix to Multiple Descent in Over-parameterized Regression

A new arXiv paper examines multiple descent phenomena in over-parameterized linear regression, a setting where prior work typically assumed independent covariates and non-degenerate covariance matrices. The authors relax both assumptions, showing that the zero pattern of the design matrix governs this behavior. The result offers a more general theoretical account of how model complexity affects prediction error.

research/regression/theory/arxiv