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CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks.

, , , , and . AAAI, page 3240-3247. AAAI Press, (2019)

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CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks., , , , and . CoRR, (2018)Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective., , , , , , and . IJCAI, page 3961-3967. ijcai.org, (2019)Towards Verifying Robustness of Neural Networks Against Semantic Perturbations., , , , and . CoRR, (2019)PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach., , , , , and . CoRR, (2018)Prediction without Preclusion: Recourse Verification with Reachable Sets., , , and . CoRR, (2023)CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks., and . CoRR, (2022)The Importance of Prompt Tuning for Automated Neuron Explanations., , , , , and . CoRR, (2023)Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations., , , , and . CVPR, page 241-249. Computer Vision Foundation / IEEE, (2020)On Extensions of Clever: A Neural Network Robustness Evaluation Algorithm., , , , , and . GlobalSIP, page 1159-1163. IEEE, (2018)Towards Fast Computation of Certified Robustness for ReLU Networks., , , , , , , and . ICML, volume 80 of Proceedings of Machine Learning Research, page 5273-5282. PMLR, (2018)