Geometric modeling of Occam's razor explains deep learning generalization
FrnkNlsn · x · 2026-08-25
An open-access paper in "Information Geometry" proposes a geometric information-theoretic approach to explain why DNNs perform well despite high parameter complexity. By modeling the parameter space as a manifold using singular semi-Riemannian geometry and analyzing the Fisher information matrix, the authors derive complexity measures that yield short description lengths, explaining DNN generalization.
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