Improving Robustness Against Adversarial Attacks with Deeply Quantized Neural Networks
Authors: Ferheen Ayaz, Idris Zakariyya, José Cano, Sye Loong Keoh, Jeremy Singer, Danilo Pau, Mounia Kharbouche-Harrari | Published: 2023-04-25
ロバスト性に関する評価
Fast Yet Effective Machine Unlearning
Authors: Ayush K Tarun, Vikram S Chundawat, Murari Mandal, Mohan Kankanhalli | Published: 2021-11-17 | Updated: 2023-05-31
Multi-concept adversarial attacks
Authors: Vibha Belavadi, Yan Zhou, Murat Kantarcioglu, Bhavani M. Thuraisingham | Published: 2021-10-19
Adversarial Attacks on ML Defense Models Competition
Authors: Yinpeng Dong, Qi-An Fu, Xiao Yang, Wenzhao Xiang, Tianyu Pang, Hang Su, Jun Zhu, Jiayu Tang, Yuefeng Chen, XiaoFeng Mao, Yuan He, Hui Xue, Chao Li, Ye Liu, Qilong Zhang, Lianli Gao, Yunrui Yu, Xitong Gao, Zhe Zhao, Daquan Lin, Jiadong Lin, Chuanbiao Song, Zihao Wang, Zhennan Wu, Yang Guo, Jiequan Cui, Xiaogang Xu, Pengguang Chen | Published: 2021-10-15
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks
Authors: Hanxun Huang, Yisen Wang, Sarah Monazam Erfani, Quanquan Gu, James Bailey, Xingjun Ma | Published: 2021-10-07 | Updated: 2022-01-23
The Connection between Out-of-Distribution Generalization and Privacy of ML Models
Authors: Divyat Mahajan, Shruti Tople, Amit Sharma | Published: 2021-10-07
Principles for Evaluation of AI/ML Model Performance and Robustness
Authors: Olivia Brown, Andrew Curtis, Justin Goodwin | Published: 2021-07-06
Understanding Catastrophic Overfitting in Single-step Adversarial Training
Authors: Hoki Kim, Woojin Lee, Jaewook Lee | Published: 2020-10-05 | Updated: 2020-12-15
Lipschitz Bounded Equilibrium Networks
Authors: Max Revay, Ruigang Wang, Ian R. Manchester | Published: 2020-10-05
Probabilistic Safety for Bayesian Neural Networks
Authors: Matthew Wicker, Luca Laurenti, Andrea Patane, Marta Kwiatkowska | Published: 2020-04-21 | Updated: 2020-06-19