Robustness Verification of Deep Neural Networks Using Star-Based Reachability Analysis with Variable-Length Time Series Input-Reference-Cited by-同舟云学术

Robustness Verification of Deep Neural Networks Using Star-Based Reachability Analysis with Variable-Length Time Series Input

Published:2023 Issue: Volume: Page:170-188
ISSN:0302-9743
Container-title:Formal Methods for Industrial Critical Systems
language:
Short-container-title:

Author:

Pal Neelanjana^ORCID,Lopez Diego Manzanas^ORCID,Johnson Taylor T^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-43681-9_10

Reference46 articles.

1. Predict battery state of charge using deep learning - MATLAB & ; Simulink – mathworks.com. https://www.mathworks.com/help/deeplearning/ug/predict-soc-using-deep-learning.html

2. Prognostics center of excellence - data repository. https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/#turbofan

3. Remaining useful life estimation using convolutional neural network - MATLAB & ; Simulink – mathworks.com. https://www.mathworks.com/help/predmaint/ug/remaining-useful-life-estimation-using-convolutional-neural-network.html

4. Anderson, G., Pailoor, S., Dillig, I., Chaudhuri, S.: Optimization and abstraction: a synergistic approach for analyzing neural network robustness. In: Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, pp. 731–744 (2019)

5. Bogomolov, S., Forets, M., Frehse, G., Potomkin, K., Schilling, C.: JuliaReach: a toolbox for set-based reachability. In: Proceedings of the 22nd ACM International Conference on Hybrid Systems: Computation and Control, pp. 39–44 (2019)

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Formal Verification of Long Short-Term Memory based Audio Classifiers: A Star based Approach;Electronic Proceedings in Theoretical Computer Science;2023-11-15

2. Robustness Verification of Deep Neural Networks Using Star-Based Reachability Analysis with Variable-Length Time Series Input;Formal Methods for Industrial Critical Systems;2023