Deformed semicircle law and concentration of nonlinear random matrices for ultra-wide neural networks-Reference-Cited by-同舟云学术

Deformed semicircle law and concentration of nonlinear random matrices for ultra-wide neural networks

Published:2024-04-01 Issue:2 Volume:34 Page:
ISSN:1050-5164
Container-title:The Annals of Applied Probability
language:
Short-container-title:Ann. Appl. Probab.

Author:

Wang Zhichao¹,Zhu Yizhe²

Affiliation:

1. Department of Mathematics, University of California, San Diego

2. Department of Mathematics, University of California, Irvine

Publisher

Institute of Mathematical Statistics

Reference87 articles.

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2. MEI, S., MISIAKIEWICZ, T. and MONTANARI, A. (2022). Generalization error of random feature and kernel methods: Hypercontractivity and kernel matrix concentration. Appl. Comput. Harmon. Anal. 59 3–84.

3. BAI, Z. D. and YIN, Y. Q. (1988). Convergence to the semicircle law. Ann. Probab. 16 863–875.

4. CHEN, B. and PAN, G. (2015). CLT for linear spectral statistics of normalized sample covariance matrices with the dimension much larger than the sample size. Bernoulli 21 1089–1133.

5. CHEN, B. B. and PAN, G. M. (2012). Convergence of the largest eigenvalue of normalized sample covariance matrices when p and n both tend to infinity with their ratio converging to zero. Bernoulli 18 1405–1420.

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