PRIMA: general and precise neural network certification via scalable convex hull approximations

Formal verification of neural networks is critical for their safe adoption in real-world applications. However, designing a precise and scalable verifier which can handle different activation functions, realistic network architectures and relevant specifications remains an open and difficult challen...

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Vydané v:	Proceedings of ACM on programming languages Ročník 6; číslo POPL; s. 1 - 33
Hlavní autori:	Müller, Mark Niklas, Makarchuk, Gleb, Singh, Gagandeep, Püschel, Markus, Vechev, Martin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	01.01.2022
ISSN:	2475-1421, 2475-1421
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