NeurIPS 2019poster133 citations
Controlling Neural Level Sets
Matan Atzmon, Niv Haim, Lior Yariv, Ofer Israelov, Haggai Maron, Yaron Lipman
Abstract
The level sets of neural networks represent fundamental properties such as decision boundaries of classifiers and are used to model non-linear manifold data such as curves and surfaces. Thus, methods for controlling the neural level sets could find many applications in machine learning.
BibTeX
@inproceedings{NEURIPS2019_b20bb95a,
author = {Atzmon, Matan and Haim, Niv and Yariv, Lior and Israelov, Ofer and Maron, Haggai and Lipman, Yaron},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Controlling Neural Level Sets},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/b20bb95ab626d93fd976af958fbc61ba-Paper.pdf},
volume = {32},
year = {2019}
}