NeurIPS 2020poster28 citations
Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth
Abstract
We prove dimension free representation results for neural networks with D ReLU layers under square loss for a class of functions G_D defined in the paper. These results capture the precise benefits of depth in the following sense:
BibTeX
@inproceedings{NEURIPS2020_78f7d96e,
author = {Bresler, Guy and Nagaraj, Dheeraj},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {10697--10706},
publisher = {Curran Associates, Inc.},
title = {Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/78f7d96ea21ccae89a7b581295f34135-Paper.pdf},
volume = {33},
year = {2020}
}