@inproceedings{31b9b55adce44a2880930e964e3fcd08,
title = "Neuron Pairs in Binarized Neural Networks Robustness Verification via Integer Linear Programming",
abstract = "In the context of classification, robustness verification of a neural network is the problem which consists in determining if small changes of inputs lead to a change of their assigned classes. We investigate such a problem on binarized neural networks via an integer linear programming perspective. We namely present a constraint generation framework based on disjunctive programming and complete descriptions of polytopes related to outputs of neuron pairs. We also introduce an alternative relying on specific families of facet defining inequalities. Preliminary experiments assess the performance of the latter approach against recent single neuron convexification results.",
keywords = "Cutting-plane, Disjunctive programming, Robustness verification",
author = "Dymitr Lubczyk and Jos{\'e} Neto",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 8th International Symposium on Combinatorial Optimization, ISCO 2024 ; Conference date: 22-05-2024 Through 24-05-2024",
year = "2024",
month = jan,
day = "1",
doi = "10.1007/978-3-031-60924-4\_23",
language = "English",
isbn = "9783031609237",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "305--317",
editor = "Amitabh Basu and Mahjoub, \{Ali Ridha\} and Mahjoub, \{Ali Ridha\} and \{Salazar Gonz{\'a}lez\}, \{Juan Jos{\'e}\}",
booktitle = "Combinatorial Optimization - 8th International Symposium, ISCO 2024, Revised Selected Papers",
}