Fetching the paper…
Reading the bibliography…
We study the Conjugate Kernel associated to a multi-layer linear-width feed-forward neural network with random weights, biases and data.
“The perceptron: a probabilistic model for information storage and organization in the brain.”
Frank Rosenblatt · 1958
Earlier work this paper cites.
“Orthogonal functions”
Giovanni Sansone · 1959
Earlier work this paper cites.
“Distribution of eigenvalues for some sets of random matrices”
V Marčenko and L Pastur · 1967
Earlier work this paper cites.
“Matrix theory and applications”
Charles Johnson · 1990
Earlier work this paper cites.
“Free convolution of measures with unbounded support”
Hari Bercovici and Dan Voiculescu · 1993
Earlier work this paper cites.
“Necessary and sufficient condition that the limit of Stieltjes transforms is a Stieltjes transform”
Jeffrey Geronimo and Theodore Hill · 2003
Earlier work this paper cites.
“Linear spectral statistics of eigenvectors of anisotropic sample covariance matrices”, 2020
Fan Yang · 2005
Earlier work this paper cites.
“Spectra of large block matrices”, 2006
Reza Far, Tamer Oraby, Wlodzimierz Bryc and Roland Speicher · 2006
Earlier work this paper cites.
“Deterministic equivalents for certain functionals of large random matrices”
Walid Hachem, Philippe Loubaton and Jamal Najim · 2007
Earlier work this paper cites.
“Isotropic local laws for sample covariance and generalized Wigner matrices”
Bloemendal Alex et al · 2014
Earlier work this paper cites.
“Universality of covariance matrices”, 2014
Natesh Pillai and Jun Yin · 2014
Cited alongside, same era.
“Anisotropic local laws for random matrices”
Antti Knowles and Jun Yin · 2017
Cited alongside, same era.
“Free probability and random matrices”
James Mingo and Roland Speicher · 2017
Cited alongside, same era.
“Nonlinear random matrix theory for deep learning”
Jeffrey Pennington and Pratik Worah · 2017
Cited alongside, same era.
“Concentration of Measure and Large Random Matrices with an application to Sample Covariance Matrices”
Cosme Louart and Romain Couillet · 2018
Cited alongside, same era.
“A note on the Pennington-Worah distribution”
S. Péché · 2019
Cited alongside, same era.
“Concentration of solutions to random equations with concentration of measure hypotheses”
Cosme Louart and Romain Couillet · 2020
Later among the works it cites.
“Global convergence of deep networks with one wide layer followed by pyramidal topology”
Quynh Nguyen and Marco Mondelli · 2020
Later among the works it cites.
“Eigenvalue distribution of some nonlinear models of random matrices”
Lucas Benigni and Sandrine Péché · 2021
Later among the works it cites.
Cosme Louart and Romain Couillet · 2021
Later among the works it cites.
“Spectral measures of spiked random matrices”
Nathan Noiry · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Greg Yang and Hadi Salman · 2019
Cited alongside, same era.
“High-dimensional dynamics of generalization error in neural networks”
Madhu Advani, Andrew Saxe and Haim Sompolinsky · 2020
Cited alongside, same era.
“Hölder continuity of cumulative distribution functions for noncommutative polynomials under finite free Fisher information”
Marwa Banna and Tobias Mai · 2020
Cited alongside, same era.
“Spectra of the Conjugate Kernel and Neural Tangent Kernel for linear-width neural networks”
Zhou Fan and Zhichao Wang · 2020
Cited alongside, same era.
“Analysis of one-hidden-layer neural networks via the resolvent method”
Vanessa Piccolo and Dominik Schröder · 2021
Later among the works it cites.
“Largest Eigenvalues of the Conjugate Kernel of Single-Layered Neural Networks”, 2022
Lucas Benigni and Sandrine Péché · 2022
Later among the works it cites.
Clément Chouard · 2022
Later among the works it cites.
“Deterministic equivalent and error universality of deep random features learning”, 2023
Dominik Schröder, Hugo Cui, Daniil Dmitriev and Bruno Loureiro · 2023
Closest in time.
Zhichao Wang and Yizhe Zhu · 2023
Closest in time.