Learning DNN Abstractions using Gradient Descent

Deep Neural Networks (DNNs) are being trained and trusted for performing fairly complex tasks, even in business- and safety-critical applications. This necessitates that they be formally analyzed before deployment. Scalability of such analyses is a major bottleneck in their widespread use. There has...

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Bibliographische Detailangaben

Veröffentlicht in:	IEEE/ACM International Conference on Automated Software Engineering : [proceedings] S. 2299 - 2303
Hauptverfasser:	Mukhopadhyay, Diganta, Siddiqui, Sanaa, Karmarkar, Hrishikesh, Madhukar, Kumar, Katz, Guy
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	ACM 27.10.2024
Schlagworte:	Abstraction Artificial neural networks Deep Neural Networks Formal Verification Limiting Model checking Scalability Software engineering Software safety
ISSN:	2643-1572
Online-Zugang:	Volltext
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