NeurIPS 2019poster100 citations
Input Similarity from the Neural Network Perspective
Guillaume Charpiat, Nicolas Girard, Loris Felardos, Yuliya Tarabalka
Abstract
Given a trained neural network, we aim at understanding how similar it considers any two samples. For this, we express a proper definition of similarity from the neural network perspective (i.e. we quantify how undissociable two inputs A and B are), by taking a machine learning viewpoint: how much a parameter variation designed to change the output for A would impact the output for B as well?
BibTeX
@inproceedings{NEURIPS2019_c61f571d,
author = {Charpiat, Guillaume and Girard, Nicolas and Felardos, Loris and Tarabalka, Yuliya},
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 = {Input Similarity from the Neural Network Perspective},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/c61f571dbd2fb949d3fe5ae1608dd48b-Paper.pdf},
volume = {32},
year = {2019}
}