Growing axons: greedy learning of neural networks with application to function approximation
Machine Learning
2020-02-18 v2 Numerical Analysis
Numerical Analysis
Machine Learning
Abstract
We propose a new method for learning deep neural network models that is based on a greedy learning approach: we add one basis function at a time, and a new basis function is generated as a non-linear activation function applied to a linear combination of the previous basis functions. Such a method (growing deep neural network by one neuron at a time) allows us to compute much more accurate approximants for several model problems in function approximation.
Cite
@article{arxiv.1910.12686,
title = {Growing axons: greedy learning of neural networks with application to function approximation},
author = {Daria Fokina and Ivan Oseledets},
journal= {arXiv preprint arXiv:1910.12686},
year = {2020}
}