“Marabou 2.0: A Versatile Formal Analyzer of Neural Networks” by Haoze Wu, Omri Isac, Aleksandar Zeljic, Teruhiro Tagomori, Matthew Daggitt, Wen Kokke, Idan Refaeli, Guy Amir, Kyle Julian, Shahaf Bassan, Pei Huang, Ori Lahav, Min Wu, Min Zhang, Ekaterina Komendantskaya, Guy Katz, and Clark Barrett. In Proceedings of the 36^th International Conference on Computer Aided Verification (CAV '24), (Arie Gurfinkel and Vijay Ganesh, eds.), July 2024, pp. 249-264. Montreal, Canada.
This paper serves as a comprehensive system description of version 2.0 of the Marabou framework for formal analysis of neural networks. We discuss the tool's architectural design and highlight the major features and components introduced since its initial release.
BibTeX entry:
@inproceedings{WIZ+24,
author = {Haoze Wu and Omri Isac and Aleksandar Zelji{\'c} and Teruhiro
Tagomori and Matthew Daggitt and Wen Kokke and Idan Refaeli and
Guy Amir and Kyle Julian and Shahaf Bassan and Pei Huang and Ori
Lahav and Min Wu and Min Zhang and Ekaterina Komendantskaya and
Guy Katz and Clark Barrett},
editor = {Arie Gurfinkel and Vijay Ganesh},
title = {Marabou 2.0: A Versatile Formal Analyzer of Neural Networks},
booktitle = {Proceedings of the {\it 36^{th}} International Conference
on Computer Aided Verification (CAV '24)},
series = {Lecture Notes in Computer Science},
volume = {14681},
pages = {249--264},
publisher = {Springer},
month = jul,
year = {2024},
note = {Montreal, Canada},
url = {http://theory.stanford.edu/~barrett/pubs/WIZ+24.pdf}
}
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