… norm, W ∈ Rn×p is a matrix of independent zero-mean unit … lemma arising from nonasymptotic
random matrix arguments, … ple covariance matrix models, involving notably the moment …

… as a structured random matrix and we study its eigenvalues in the context of random matrix
theory, using tools from free probability. We focus on singlehidden-layer networks, but in …

… We will use advanced methods from Random Matrix Theory (RMT), developed in the theory
of self organizing systems, to characterize DNN layer weight matrices, Wl,2 during and after …

… door for direct applications of random matrix theory to deep … neural networks can be
incorporated into a standard method of proof in random matrix theory known as the moments method…

… This class of neural networks attractive and valuable to the data mining … neural networks
and kernel functions. A clear exposition of the basic framework underlying all these approaches …

… spectra of random matrices do not apply to synaptic connectivity matrices because of the …
Therefore, we compute eigenvalue spectra of large random matrices with excitatory and …

… We study products of random matrices in the regime where the number of terms and the size
of the matrices simultaneously tend to infinity. Our main theorem is that the logarithm of the \(…

… Here we present a systematic method based on path integrals which overcomes … the method
to a large nonlinear rate-based neural network with random asymmetric connectivity matrix. …

… , when applied to kernels derived from large-scale neural networks (eg, ResNet-50) and real
… whenever a local random matrix law holds. Finally, we apply this random matrix theory lens …

… For example, in 1977 Grenander and Silverstein [101] rediscovered (1.10) motivated by a
neural network problem where the entries of H take only two values. Also unaware of the in-…