Privacy-preserving parametric inference: a case for robust statistics
Authors: Marco Avella-Medina | Published: 2019-11-22
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Interpreting and Evaluating Neural Network Robustness
Authors: Fuxun Yu, Zhuwei Qin, Chenchen Liu, Liang Zhao, Yanzhi Wang, Xiang Chen | Published: 2019-05-10
On the Connection Between Adversarial Robustness and Saliency Map Interpretability
Authors: Christian Etmann, Sebastian Lunz, Peter Maass, Carola-Bibiane Schönlieb | Published: 2019-05-10
Adversarial Defense Framework for Graph Neural Network
Authors: Shen Wang, Zhengzhang Chen, Jingchao Ni, Xiao Yu, Zhichun Li, Haifeng Chen, Philip S. Yu | Published: 2019-05-09 | Updated: 2019-05-10
Adversarial Examples Are Not Bugs, They Are Features
Authors: Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry | Published: 2019-05-06 | Updated: 2019-08-12
Weight Map Layer for Noise and Adversarial Attack Robustness
Authors: Mohammed Amer, Tomás Maul | Published: 2019-05-02 | Updated: 2020-12-02
Adversarial Training and Robustness for Multiple Perturbations
Authors: Florian Tramèr, Dan Boneh | Published: 2019-04-30 | Updated: 2019-10-18
Deep pNML: Predictive Normalized Maximum Likelihood for Deep Neural Networks
Authors: Koby Bibas, Yaniv Fogel, Meir Feder | Published: 2019-04-28 | Updated: 2020-01-08
Learning with Bad Training Data via Iterative Trimmed Loss Minimization
Authors: Yanyao Shen, Sujay Sanghavi | Published: 2018-10-28 | Updated: 2019-02-18
Towards Robust Deep Neural Networks
Authors: Timothy E. Wang, Yiming Gu, Dhagash Mehta, Xiaojun Zhao, Edgar A. Bernal | Published: 2018-10-27 | Updated: 2018-12-04