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NNV: The Neural Network Verification Tool for Deep Neural Networks and Learning-Enabled Cyber-Physical Systems.

, , , , , , , и . CAV (1), том 12224 из Lecture Notes in Computer Science, стр. 3-17. Springer, (2020)

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Improved Geometric Path Enumeration for Verifying ReLU Neural Networks., , , и . CAV (1), том 12224 из Lecture Notes in Computer Science, стр. 66-96. Springer, (2020)Neural Network Compression of ACAS Xu is Unsafe: Closed-Loop Verification through Quantized State Backreachability., и . CoRR, (2022)Neural Network Repair with Reachability Analysis., , , , , и . CoRR, (2021)Verification of Recurrent Neural Networks with Star Reachability., , , , , и . HSCC, стр. 6:1-6:13. ACM, (2023)Benchmark: A Nonlinear Reachability Analysis Test Set from Numerical Analysis., , и . ARCH@CPSWeek, том 34 из EPiC Series in Computing, стр. 89-97. EasyChair, (2015)Verification of Closed-loop Systems with Neural Network Controllers., , , и . ARCH@CPSIoTWeek, том 61 из EPiC Series in Computing, стр. 201-210. EasyChair, (2019)Parallelizable reachability analysis algorithms for feed-forward neural networks., , , , , , и . FormaliSE@ICSE, стр. 31-40. IEEE / ACM, (2019)Star-Based Reachability Analysis of Deep Neural Networks., , , , , , и . FM, том 11800 из Lecture Notes in Computer Science, стр. 670-686. Springer, (2019)On reachable set estimation for discrete-time switched linear systems under arbitrary switching., , и . ACC, стр. 4534-4539. IEEE, (2017)NNV: The Neural Network Verification Tool for Deep Neural Networks and Learning-Enabled Cyber-Physical Systems., , , , , , , и . CAV (1), том 12224 из Lecture Notes in Computer Science, стр. 3-17. Springer, (2020)