A Framework for Including Uncertainty in Robustness Evaluation of Bayesian Neural Network Classifiers-Reference-Cited by-同舟云学术

A Framework for Including Uncertainty in Robustness Evaluation of Bayesian Neural Network Classifiers

Published:2024-04-20 Issue: Volume: Page:25-32
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Container-title:Proceedings of the 5th IEEE/ACM International Workshop on Deep Learning for Testing and Testing for Deep Learning
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Author:

Essbai Wasim¹^ORCID,Bombarda Andrea²^ORCID,Bonfanti Silvia²^ORCID,Gargantini Angelo²^ORCID

Affiliation:

1. Cyber-Physical Systems Group, Technische Universitat Wien, Vienna, Vienna, Austria

2. Ingegneria Gestionale, dell'Informazione e della Produzione, University of Bergamo, Bergamo, Bergamo, Italy

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3643786.3648026

Reference28 articles.

1. S. Albawi, T. A. Mohammed, and S. Al-Zawi. 2017. Understanding of a convolutional neural network. In 2017 Int. Conf. on Engineering and Technology (ICET). 1--6.

2. P. Arcaini, A. Bombarda, S. Bonfanti, and A. Gargantini. 2020. Dealing with Robustness of Convolutional Neural Networks for Image Classification. In 2020 IEEE International Conference On Artificial Intelligence Testing (AITest). 7--14.

3. P. Arcaini, A. Bombarda, S. Bonfanti, and A. Gargantini. 2021. ROBY: a Tool for Robustness Analysis of Neural Network Classifiers. In 2021 14th IEEE Conference on Software Testing, Verification and Validation (ICST). 442--447.

4. P. Arcaini, A. Bombarda, S. Bonfanti, A. Gargantini, D. Gamba, and R. Pedercini. 2022. Robustness assessment and improvement of a neural network for blood oxygen pressure estimation. In 2022 IEEE Conference on Software Testing, Verification and Validation (ICST). IEEE Computer Society.

5. Variational Inference: A Review for Statisticians