Network-size independent covering number bounds for deep networks
Machine Learning
2017-11-10 v2 Machine Learning
Abstract
We give a covering number bound for deep learning networks that is independent of the size of the network. The key for the simple analysis is that for linear classifiers, rotating the data doesn't affect the covering number. Thus, we can ignore the rotation part of each layer's linear transformation, and get the covering number bound by concentrating on the scaling part.
Keywords
Cite
@article{arxiv.1711.00753,
title = {Network-size independent covering number bounds for deep networks},
author = {Mayank Kabra and Kristin Branson},
journal= {arXiv preprint arXiv:1711.00753},
year = {2017}
}
Comments
We found a possible error in our analysis. We are re-evaluating, and may resubmit