Concentration of measure for non-linear random matrices with applications to neural networks and non-commutative polynomials
Probability
2025-07-15 v2 Machine Learning
Abstract
We prove concentration inequalities for several models of non-linear random matrices. As corollaries we obtain estimates for linear spectral statistics of the conjugate kernel of neural networks and non-commutative polynomials in (possibly dependent) random matrices.
Keywords
Cite
@article{arxiv.2507.07625,
title = {Concentration of measure for non-linear random matrices with applications to neural networks and non-commutative polynomials},
author = {Radosław Adamczak},
journal= {arXiv preprint arXiv:2507.07625},
year = {2025}
}
Comments
Some typos fixed (and some new probably introduced), small editorial changes