暂无分享,去创建一个
David J. Fleet | Nicolas Le Roux | Fabian Pedregosa | Fartash Faghri | Cristina Vasconcelos | Fartash Faghri | Fabian Pedregosa | C. Vasconcelos
[1] Timothy A. Mann,et al. On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models , 2018, ArXiv.
[2] Andrea Montanari,et al. The generalization error of max-margin linear classifiers: High-dimensional asymptotics in the overparametrized regime , 2019 .
[3] Kaizhu Huang,et al. A Unified Gradient Regularization Family for Adversarial Examples , 2015, 2015 IEEE International Conference on Data Mining.
[4] Sung-Ho Bae,et al. Towards an Adversarially Robust Normalization Approach , 2019, ArXiv.
[5] J. Zico Kolter,et al. Provable defenses against adversarial examples via the convex outer adversarial polytope , 2017, ICML.
[6] Andrea Montanari,et al. Surprises in High-Dimensional Ridgeless Least Squares Interpolation , 2019, Annals of statistics.
[7] Issei Sato,et al. On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[8] J. Zico Kolter,et al. Adversarial Robustness Against the Union of Multiple Perturbation Models , 2019, ICML.
[9] Aleksander Madry,et al. Adversarial Examples Are Not Bugs, They Are Features , 2019, NeurIPS.
[10] Provable tradeoffs in adversarially robust classification , 2020, ArXiv.
[11] Seyed-Mohsen Moosavi-Dezfooli,et al. Robustness via Curvature Regularization, and Vice Versa , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Bernhard Schölkopf,et al. First-Order Adversarial Vulnerability of Neural Networks and Input Dimension , 2018, ICML.
[13] Cho-Jui Hsieh,et al. A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks , 2019, NeurIPS.
[14] Xiaojin Zhu,et al. Should Adversarial Attacks Use Pixel p-Norm? , 2019, ArXiv.
[15] John Duchi,et al. Understanding and Mitigating the Tradeoff Between Robustness and Accuracy , 2020, ICML.
[16] Ekin D. Cubuk,et al. A Fourier Perspective on Model Robustness in Computer Vision , 2019, NeurIPS.
[17] Avery Ma,et al. Adversarial Robustness through Regularization: A Second-Order Approach , 2020, ArXiv.
[18] Matthias Hein,et al. Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks , 2020, ICML.
[19] Jean-Philippe Vial,et al. Robust Optimization , 2021, ICORES.
[20] Ruitong Huang,et al. Max-Margin Adversarial (MMA) Training: Direct Input Space Margin Maximization through Adversarial Training , 2018, ICLR.
[21] Wilhelm Burger,et al. Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.
[22] Nathan Srebro,et al. Characterizing Implicit Bias in Terms of Optimization Geometry , 2018, ICML.
[23] Timothy A. Mann,et al. Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples , 2020, ArXiv.
[24] Dan Boneh,et al. Adversarial Training and Robustness for Multiple Perturbations , 2019, NeurIPS.
[25] Rafael C. González,et al. Digital image processing, 3rd Edition , 2008 .
[26] Pushmeet Kohli,et al. Adversarial Robustness through Local Linearization , 2019, NeurIPS.
[27] Nathan Srebro,et al. A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case , 2019, ICLR.
[28] Aleksander Madry,et al. Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.
[29] Andrew Slavin Ross,et al. Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients , 2017, AAAI.
[30] Martin Wattenberg,et al. Adversarial Spheres , 2018, ICLR.
[31] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[32] Matus Telgarsky,et al. Margins, Shrinkage, and Boosting , 2013, ICML.
[33] Nathan Srebro,et al. Implicit Bias of Gradient Descent on Linear Convolutional Networks , 2018, NeurIPS.
[34] Adel Javanmard,et al. Precise Tradeoffs in Adversarial Training for Linear Regression , 2020, COLT.
[35] Adel Javanmard,et al. Precise Statistical Analysis of Classification Accuracies for Adversarial Training , 2020, ArXiv.
[36] Christos Thrampoulidis,et al. A Model of Double Descent for High-dimensional Binary Linear Classification , 2019, ArXiv.
[37] Hamza Fawzi,et al. Adversarial vulnerability for any classifier , 2018, NeurIPS.
[38] Boaz Barak,et al. Deep double descent: where bigger models and more data hurt , 2019, ICLR.
[39] J. Zico Kolter,et al. Certified Adversarial Robustness via Randomized Smoothing , 2019, ICML.
[40] Ludwig Schmidt,et al. Unlabeled Data Improves Adversarial Robustness , 2019, NeurIPS.
[41] Aleksander Madry,et al. Robustness May Be at Odds with Accuracy , 2018, ICLR.
[42] Lujo Bauer,et al. On the Suitability of Lp-Norms for Creating and Preventing Adversarial Examples , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[43] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[44] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[45] Tuo Zhao,et al. Implicit Bias of Gradient Descent based Adversarial Training on Separable Data , 2020, ICLR.
[46] Graham W. Taylor,et al. Batch Normalization is a Cause of Adversarial Vulnerability , 2019, ArXiv.
[47] Stephen P. Boyd,et al. CVXPY: A Python-Embedded Modeling Language for Convex Optimization , 2016, J. Mach. Learn. Res..
[48] Ji Zhu,et al. Margin Maximizing Loss Functions , 2003, NIPS.
[49] Martha Larson,et al. Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter , 2020, BMVC.
[50] Ankit B. Patel,et al. Using Learning Dynamics to Explore the Role of Implicit Regularization in Adversarial Examples , 2020, ArXiv.
[51] Guillermo Sapiro,et al. Robust Large Margin Deep Neural Networks , 2017, IEEE Transactions on Signal Processing.
[52] Long Chen,et al. On Connections Between Regularizations for Improving DNN Robustness , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[53] David A. Wagner,et al. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples , 2018, ICML.
[54] Hossein Mobahi,et al. A Unifying View on Implicit Bias in Training Linear Neural Networks , 2021, ICLR.
[55] Kaifeng Lyu,et al. Gradient Descent Maximizes the Margin of Homogeneous Neural Networks , 2019, ICLR.
[56] Matus Telgarsky,et al. Gradient descent aligns the layers of deep linear networks , 2018, ICLR.
[57] Michael I. Jordan,et al. Theoretically Principled Trade-off between Robustness and Accuracy , 2019, ICML.
[58] Chun-Liang Li,et al. Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer , 2018, ICLR.
[59] John Shawe-Taylor,et al. Structural Risk Minimization Over Data-Dependent Hierarchies , 1998, IEEE Trans. Inf. Theory.
[60] Liang Liang,et al. Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks , 2020, ArXiv.
[61] Quoc V. Le,et al. Smooth Adversarial Training , 2020, ArXiv.
[62] Marcus A. Brubaker,et al. On the Effectiveness of Low Frequency Perturbations , 2019, IJCAI.
[63] Aleksander Madry,et al. On Adaptive Attacks to Adversarial Example Defenses , 2020, NeurIPS.
[64] Matus Telgarsky,et al. Directional convergence and alignment in deep learning , 2020, NeurIPS.
[65] Matthias Hein,et al. Adversarial Robustness on In- and Out-Distribution Improves Explainability , 2020, ECCV.
[66] Seyed-Mohsen Moosavi-Dezfooli,et al. Hold me tight! Influence of discriminative features on deep network boundaries , 2020, NeurIPS.
[67] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[68] Nathan Srebro,et al. Convergence of Gradient Descent on Separable Data , 2018, AISTATS.
[69] Nicolas Le Roux,et al. An Effective Anti-Aliasing Approach for Residual Networks , 2020, ArXiv.
[70] Pascal Frossard,et al. Analysis of classifiers’ robustness to adversarial perturbations , 2015, Machine Learning.
[71] Julien Mairal,et al. Structured sparsity through convex optimization , 2011, ArXiv.
[72] Kilian Q. Weinberger,et al. Low Frequency Adversarial Perturbation , 2018, UAI.
[73] Colin Wei,et al. Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel , 2018, NeurIPS.
[74] Yisen Wang,et al. Adversarial Weight Perturbation Helps Robust Generalization , 2020, NeurIPS.
[75] Aleksander Madry,et al. Adversarially Robust Generalization Requires More Data , 2018, NeurIPS.
[76] Samy Bengio,et al. Understanding deep learning requires rethinking generalization , 2016, ICLR.
[77] Matthias Hein,et al. Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation , 2017, NIPS.
[78] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[79] Francis Bach,et al. Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss , 2020, COLT.
[80] Hossein Mobahi,et al. Large Margin Deep Networks for Classification , 2018, NeurIPS.
[81] Prateek Mittal,et al. RobustBench: a standardized adversarial robustness benchmark , 2020, ArXiv.