Verification and Design of Robust and Safe Neural Network-enabled Autonomous Systems-Reference-Cited by-同舟云学术

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Verification and Design of Robust and Safe Neural Network-enabled Autonomous Systems

Published:2023-09-26 Issue: Volume: Page:
ISSN:
Container-title:2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
language:
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Author:

Zhu Qi¹,Li Wenchao²,Huang Chao³,Chen Xin⁴,Zhou Weichao²,Wang Yixuan¹,Li Jiajun³,Fu Feisi²

Affiliation:

1. Northwestern University,Department of Electrical and Computer Engineering,USA

2. Boston University,Department of Electrical and Computer Engineering,USA

3. University of Liverpool,Department of Computer Science,UK

4. University of New Mexico,Department of Computer Science,USA

Funder

National Science Foundation

Air Force Research Laboratory

Engineering and Physical Sciences Research Council

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10313338/10313355/10313451.pdf?arnumber=10313451

Reference78 articles.

1. Parsimonious, Simulation Based Verification of Linear Systems

2. Efficient Representation and Computation of Reachable Sets for Hybrid Systems

3. Risk-constrained reinforcement learning with percentile risk criteria;chow;The Journal of Machine Learning Research,2017

4. Fast and effective robustness certification;singh;NeurIPS,2018

5. Output Range Analysis for Deep Feedforward Neural Networks

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