暂无分享,去创建一个
[1] Alexandros G. Dimakis,et al. Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes , 2019, NeurIPS.
[2] Soheil Feizi,et al. Bounding Singular Values of Convolution Layers , 2019, ArXiv.
[3] Maneesh Kumar Singh,et al. On Lipschitz Bounds of General Convolutional Neural Networks , 2018, IEEE Transactions on Information Theory.
[4] Ananthram Swami,et al. The Limitations of Deep Learning in Adversarial Settings , 2015, 2016 IEEE European Symposium on Security and Privacy (EuroS&P).
[5] Frank Allgöwer,et al. Training Robust Neural Networks Using Lipschitz Bounds , 2020, IEEE Control Systems Letters.
[6] Philip M. Long,et al. The Singular Values of Convolutional Layers , 2018, ICLR.
[7] Russ Tedrake,et al. Verifying Neural Networks with Mixed Integer Programming , 2017, ArXiv.
[8] Corina S. Pasareanu,et al. Fast Geometric Projections for Local Robustness Certification , 2020, ICLR.
[9] Adam M. Oberman,et al. Scaleable input gradient regularization for adversarial robustness , 2019, Machine Learning with Applications.
[10] Cyrus Rashtchian,et al. A Closer Look at Accuracy vs. Robustness , 2020, NeurIPS.
[11] Mislav Balunovic,et al. Adversarial Training and Provable Defenses: Bridging the Gap , 2020, ICLR.
[12] Ya Le,et al. Tiny ImageNet Visual Recognition Challenge , 2015 .
[13] Aditi Raghunathan,et al. Certified Defenses against Adversarial Examples , 2018, ICLR.
[14] Matthew Mirman,et al. Differentiable Abstract Interpretation for Provably Robust Neural Networks , 2018, ICML.
[15] Michael I. Jordan,et al. Theoretically Principled Trade-off between Robustness and Accuracy , 2019, ICML.
[16] Cho-Jui Hsieh,et al. Efficient Neural Network Robustness Certification with General Activation Functions , 2018, NeurIPS.
[17] Matthias Hein,et al. Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation , 2017, NIPS.
[18] Manfred Morari,et al. Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks , 2019, NeurIPS.
[19] Pradeep Ravikumar,et al. MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius , 2020, ICLR.
[20] Greg Yang,et al. Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers , 2019, NeurIPS.
[21] Todd Huster,et al. Universal Lipschitz Approximation in Bounded Depth Neural Networks , 2019, ArXiv.
[22] Yuichi Yoshida,et al. Spectral Normalization for Generative Adversarial Networks , 2018, ICLR.
[23] Aleksander Madry,et al. Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.
[24] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[25] Moustapha Cissé,et al. Parseval Networks: Improving Robustness to Adversarial Examples , 2017, ICML.
[26] J. Zico Kolter,et al. Provable defenses against adversarial examples via the convex outer adversarial polytope , 2017, ICML.
[27] Masashi Sugiyama,et al. Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks , 2018, NeurIPS.
[28] J. Zico Kolter,et al. Certified Adversarial Robustness via Randomized Smoothing , 2019, ICML.
[29] Bernhard Pfahringer,et al. Regularisation of neural networks by enforcing Lipschitz continuity , 2018, Machine Learning.
[30] Pushmeet Kohli,et al. Adversarial Robustness through Local Linearization , 2019, NeurIPS.
[31] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[32] Cho-Jui Hsieh,et al. Towards Stable and Efficient Training of Verifiably Robust Neural Networks , 2019, ICLR.
[33] Inderjit S. Dhillon,et al. Towards Fast Computation of Certified Robustness for ReLU Networks , 2018, ICML.
[34] J. Zico Kolter,et al. Orthogonalizing Convolutional Layers with the Cayley Transform , 2021, ICLR.
[35] David Tse,et al. Generalizable Adversarial Training via Spectral Normalization , 2018, ICLR.
[36] Ritu Chadha,et al. Limitations of the Lipschitz constant as a defense against adversarial examples , 2018, Nemesis/UrbReas/SoGood/IWAISe/GDM@PKDD/ECML.
[37] Jaewook Lee,et al. Lipschitz-Certifiable Training with a Tight Outer Bound , 2020, NeurIPS.
[38] Cem Anil,et al. Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks , 2019, NeurIPS.
[39] Swarat Chaudhuri,et al. AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation , 2018, 2018 IEEE Symposium on Security and Privacy (SP).
[40] Sven Gowal,et al. Scalable Verified Training for Provably Robust Image Classification , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[41] Matteo Fischetti,et al. Deep neural networks and mixed integer linear optimization , 2018, Constraints.
[42] Cem Anil,et al. Sorting out Lipschitz function approximation , 2018, ICML.
[43] J. Zico Kolter,et al. Scaling provable adversarial defenses , 2018, NeurIPS.
[44] Jinfeng Yi,et al. Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach , 2018, ICLR.
[45] Suman Jana,et al. Certified Robustness to Adversarial Examples with Differential Privacy , 2018, 2019 IEEE Symposium on Security and Privacy (SP).
[46] Matthias Hein,et al. Provable Robustness of ReLU networks via Maximization of Linear Regions , 2018, AISTATS.
[47] David Wagner,et al. Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods , 2017, AISec@CCS.