veriFIRE: Verifying an Industrial, Learning-Based Wildfire Detection System-Reference-Cited by-同舟云学术

veriFIRE: Verifying an Industrial, Learning-Based Wildfire Detection System

Published:2023 Issue: Volume: Page:648-656
ISSN:0302-9743
Container-title:Formal Methods
language:
Short-container-title:

Author:

Amir Guy,Freund Ziv,Katz Guy,Mandelbaum Elad,Refaeli Idan

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-27481-7_38

Reference51 articles.

1. Amir, G., et al.: Verifying Learning-Based Robotic Navigation Systems (2022). Technical report. https://arxiv.org/abs/2205.13536

2. Amir, G., Schapira, M., Katz, G.: Towards scalable verification of deep reinforcement learning. In: Proceedings 21st International Conference on Formal Methods in Computer-Aided Design (FMCAD), pp. 193–203 (2021)

3. Lecture Notes in Computer Science;G Amir,2021

4. Amir, G., Zelazny, T., Katz, G., Schapira, M.: Verification-aided deep ensemble selection. In: Proceedings of the 22nd International Conference on Formal Methods in Computer-Aided Design (FMCAD), pp. 27–37 (2022)

5. Baluta, T., Shen, S., Shinde, S., Meel, K.S., Saxena, P.: Quantitative verification of neural networks and its security applications. In: Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (CCS), pp. 1249–1264 (2019)

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