Marabou 2.0: A Versatile Formal Analyzer of Neural Networks
Haoze Wu, Omri Isac, Aleksandar Zelji\'c, 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
TL;DR
Marabou 2.0 is a comprehensive framework for formal analysis of neural networks, featuring new architectural components and capabilities that enhance its versatility and effectiveness.
Contribution
This paper introduces Marabou 2.0, a significantly improved version with new features and architecture for neural network formal analysis.
Findings
Enhanced architectural design for neural network analysis
Introduction of new features and components in Marabou 2.0
Improved versatility and effectiveness of the framework
Abstract
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.
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Taxonomy
TopicsNeural Networks and Applications