NeurIPS 2017poster729 citations
Decoupling "when to update" from "how to update"
Eran Malach, Shai Shalev-Shwartz
Abstract
Deep learning requires data. A useful approach to obtain data is to be creative and mine data from various sources, that were created for different purposes. Unfortunately, this approach often leads to noisy labels. In this paper, we propose a meta algorithm for tackling the noisy labels problem. The key idea is to decouple
BibTeX
@inproceedings{NIPS2017_58d4d1e7,
author = {Malach, Eran and Shalev-Shwartz, Shai},
booktitle = {Advances in Neural Information Processing Systems},
editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Decoupling "when to update" from "how to update"},
url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/58d4d1e7b1e97b258c9ed0b37e02d087-Paper.pdf},
volume = {30},
year = {2017}
}