Web: http://arxiv.org/abs/2111.12143

Jan. 28, 2022, 2:11 a.m. | Darshil Doshi, Tianyu He, Andrey Gromov

cs.LG updates on arXiv.org arxiv.org

Deep neural networks are notorious for defying theoretical treatment.
However, when the number of parameters in each layer tends to infinity the
network function is a Gaussian process (GP) and quantitatively predictive
description is possible. Gaussian approximation allows to formulate criteria
for selecting hyperparameters, such as variances of weights and biases, as well
as the learning rate. These criteria rely on the notion of criticality defined
for deep neural networks. In this work we describe a new practical way to …

applications arxiv deep networks neural neural networks theory

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