Asymptotic properties of one-layer artificial neural networks with sparse connectivity
Disordered Systems and Neural Networks
2021-12-13 v2 Probability
Machine Learning
Abstract
A law of large numbers for the empirical distribution of parameters of a one-layer artificial neural networks with sparse connectivity is derived for a simultaneously increasing number of both, neurons and training iterations of the stochastic gradient descent.
Keywords
Cite
@article{arxiv.2112.00732,
title = {Asymptotic properties of one-layer artificial neural networks with sparse connectivity},
author = {Christian Hirsch and Matthias Neumann and Volker Schmidt},
journal= {arXiv preprint arXiv:2112.00732},
year = {2021}
}
Comments
12 pages, 4 figures