Track C1: Safety Verification of Deep Neural Networks (DNNs)
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-46002-9_12
Reference19 articles.
1. Bak, S., Liu, C., Johnson, T.: The second international verification of neural networks competition (VNN-COMP 2021): summary and results (2021). https://doi.org/10.48550/ARXIV.2109.00498
2. LNCS;S Bensalem,2023
3. Brix, C., Müller, M.N., Bak, S., Johnson, T.T., Liu, C.: First three years of the international verification of neural networks competition (VNN-COMP). Int. J. Softw. Tools Technol. Transfer 1–11 (2023)
4. LNCS;M Forets,2023
5. Johnson, T.T., et al.: ARCH-COMP21 category report: artificial intelligence and neural network control systems (AINNCS) for continuous and hybrid systems plants. In: Frehse, G., Althoff, M. (eds.) 8th International Workshop on Applied Verification of Continuous and Hybrid Systems (ARCH21). EPiC Series in Computing, vol. 80, pp. 90–119. EasyChair (2021). https://doi.org/10.29007/kfk9
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1. Unifying Syntactic and Semantic Abstractions for Deep Neural Networks;Lecture Notes in Computer Science;2024
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