The energy landscape of a simple neural network
Machine Learning
2017-06-23 v1 Machine Learning
Abstract
We explore the energy landscape of a simple neural network. In particular, we expand upon previous work demonstrating that the empirical complexity of fitted neural networks is vastly less than a naive parameter count would suggest and that this implicit regularization is actually beneficial for generalization from fitted models.
Keywords
Cite
@article{arxiv.1706.07101,
title = {The energy landscape of a simple neural network},
author = {Anthony Collins Gamst and Alden Walker},
journal= {arXiv preprint arXiv:1706.07101},
year = {2017}
}
Comments
17 pages, 15 figures