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20182022
most citedRandom Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures

20 citations · 20 across the 2 of their papers we have counts for

collaborators

3 papers

math.PR2022

Sharp Bounds for the Concentration of the Resolvent in Convex Concentration Settings

Cosme Louart

Considering random matrix with independent columns satisfying the convex concentration properties issued from a famous theorem of Talagrand, we express the…

cs.LG2020★ 20 cited

Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures

Mohamed El Amine Seddik, Cosme Louart, Mohamed Tamaazousti +1

This paper shows that deep learning (DL) representations of data produced by generative adversarial nets (GANs) are random vectors which fall within the class of so-called \textit{…

math.PR2018

Concentration of Measure and Large Random Matrices with an application to Sample Covariance Matrices

Cosme Louart, Romain Couillet

The present work provides an original framework for random matrix analysis based on revisiting the concentration of measure theory from a probabilistic point of view. By providing…