On Random Matrices Arising in Deep Neural Networks: General I.I.D. Case

20 November 2020

Papers citing "On Random Matrices Arising in Deep Neural Networks: General I.I.D. Case"

7 / 7 papers shown

Title
Towards Quantifying the Hessian Structure of Neural Networks Zhaorui Dong Yushun Zhang Z. Luo Jianfeng Yao Ruoyu Sun 26 0 0 05 May 2025
Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures Zenan Ling Longbo Li Zhanbo Feng Yixuan Zhang Feng Zhou Robert C. Qiu Zhenyu Liao 32 4 0 05 Feb 2024
Universal characteristics of deep neural network loss surfaces from random matrix theory Nicholas P. Baskerville J. Keating F. Mezzadri J. Najnudel Diego Granziol 22 4 0 17 May 2022
Concentration of Random Feature Matrices in High-Dimensions Zhijun Chen Hayden Schaeffer Rachel A. Ward 18 6 0 14 Apr 2022
Eigenvalue Distribution of Large Random Matrices Arising in Deep Neural Networks: Orthogonal Case L. Pastur 14 5 0 12 Jan 2022
Free Probability for predicting the performance of feed-forward fully connected neural networks Reda Chhaibi Tariq Daouda E. Kahn ODL 26 1 0 01 Nov 2021
On Random Matrices Arising in Deep Neural Networks. Gaussian Case L. Pastur 13 23 0 17 Jan 2020