6 citations · 12 across the 18 of their papers we have counts for
3 papers · 1 filter
FAME: Formal Abstract Minimal Explanation for Neural Networks
Ryma Boumazouza, Raya Elsaleh, Melanie Ducoffe +2
We propose FAME (Formal Abstract Minimal Explanations), a new class of abductive explanations grounded in abstract interpretation. FAME is the first method to scale to large neural…
Marabou 2.0: A Versatile Formal Analyzer of Neural Networks
Haoze Wu, Omri Isac, Aleksandar Zeljić +14
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 a…
Formally Explaining Neural Networks within Reactive Systems
Shahaf Bassan, Guy Amir, Davide Corsi +2
Deep neural networks (DNNs) are increasingly being used as controllers in reactive systems. However, DNNs are highly opaque, which renders it difficult to explain and justify their…