Safety and Robustness for Deep Learning with Provable Guarantees

Computing systems are becoming ever more complex, with decisions increasingly often based on deep learning components. A wide variety of applications are being developed, many of them safety-critical, such as self-driving cars and medical diagnosis. Since deep learning is unstable with respect to ad...

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Veröffentlicht in:	2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) S. 1 - 3
1. Verfasser:	Kwiatkowska, Marta
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	ACM 01.09.2020
Schlagworte:	Bayesian neural networks Computing methodologies → Neural networks Deep learning formal verification Neural networks Perturbation methods Robustness Safety Testing Theory of computation →Logic and verification Upper bound
ISSN:	2643-1572
Online-Zugang:	Volltext
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