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REGLO: Provable Neural Network Repair for Global Robustness Properties.

, , , , , , , , and . AAAI, page 12061-12071. AAAI Press, (2024)

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Divide and Slide: Layer-Wise Refinement for Output Range Analysis of Deep Neural Networks., , , , and . IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 39 (11): 3323-3335 (2020)POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems., , , , and . ATVA, volume 13505 of Lecture Notes in Computer Science, page 414-430. Springer, (2022)Robust Deep Reinforcement Learning via Multi-View Information Bottleneck., and . CoRR, (2021)POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems., , , , , , , , and . IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 43 (3): 994-1007 (March 2024)REGLO: Provable Neural Network Repair for Global Robustness Properties., , , , , , , , and . AAAI, page 12061-12071. AAAI Press, (2024)Towards Verification-Aware Knowledge Distillation for Neural-Network Controlled Systems: Invited Paper., , , , and . ICCAD, page 1-8. ACM, (2019)Adversarial Training and Provable Robustness: A Tale of Two Objectives., and . CoRR, (2020)ReachNN: Reachability Analysis of Neural-Network Controlled Systems., , , , and . CoRR, (2019)POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems., , , , , , , , and . CoRR, (2023)Adversarial Training and Provable Robustness: A Tale of Two Objectives., and . AAAI, page 7367-7376. AAAI Press, (2021)