A Numerical Investigation of the Minimum Width of a Neural Network
Machine Learning
2019-10-31 v1
Abstract
Neural network width and depth are fundamental aspects of network topology. Universal approximation theorems provide that with increasing width or depth, there exists a neural network that approximates a function arbitrarily well. These theorems assume requirements, such as infinite data, that must be discretized in practice. Through numerical experiments, we seek to test the lower bounds established by Hanin in 2017.
Cite
@article{arxiv.1910.13817,
title = {A Numerical Investigation of the Minimum Width of a Neural Network},
author = {Ibrohim Nosirov and Jeffrey M. Hokanson},
journal= {arXiv preprint arXiv:1910.13817},
year = {2019}
}
Comments
Draft manuscript to be submitted to SIAM Undergrad Research Online