Appearance of Random Matrix Theory in deep learning

Nicholas P. Baskerville, Diego Granziol and Jonathan P. Keating

Physica A: Statistical Mechanics and its Applications, 2022, vol. 590, issue C

Abstract: We investigate the local spectral statistics of the loss surface Hessians of artificial neural networks, where we discover agreement with Gaussian Orthogonal Ensemble statistics across several network architectures and datasets. These results shed new light on the applicability of Random Matrix Theory to modelling neural networks and suggest a role for it in the study of loss surfaces in deep learning.

Keywords: Random Matrix Theory; Deep learning; Machine learning; Neural networks; Local statistics; Wigner surmise (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:590:y:2022:i:c:s0378437121009432

DOI: 10.1016/j.physa.2021.126742

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