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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Bibliographic Details

Published in:	2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) pp. 1 - 3
Main Author:	Kwiatkowska, Marta
Format:	Conference Proceeding
Language:	English
Published:	ACM 01.09.2020
Subjects:	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 Access:	Get full text
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