Exploring the Vulnerability of Deep Neural Networks: A Study of
Parameter Corruption

Exploring the Vulnerability of Deep Neural Networks: A Study of Parameter Corruption

10 June 2020

Xuancheng Ren

Papers citing "Exploring the Vulnerability of Deep Neural Networks: A Study of Parameter Corruption"

7 / 7 papers shown

Title
Machine Unlearning Fails to Remove Data Poisoning Attacks Martin Pawelczyk Jimmy Z. Di Yiwei Lu Gautam Kamath Ayush Sekhari Seth Neel AAML MU 57 8 0 25 Jun 2024
Effective Gradient Sample Size via Variation Estimation for Accelerating Sharpness aware Minimization Jiaxin Deng Junbiao Pang Baochang Zhang Tian Wang 40 1 0 24 Feb 2024
Adversarial Parameter Defense by Multi-Step Risk Minimization Zhiyuan Zhang Ruixuan Luo Xuancheng Ren Qi Su Liangyou Li Xu Sun AAML 23 6 0 07 Sep 2021
How to Inject Backdoors with Better Consistency: Logit Anchoring on Clean Data Zhiyuan Zhang Lingjuan Lyu Weiqiang Wang Lichao Sun Xu Sun 21 35 0 03 Sep 2021
Sharpness-Aware Minimization for Efficiently Improving Generalization Pierre Foret Ariel Kleiner H. Mobahi Behnam Neyshabur AAML 48 1,276 0 03 Oct 2020
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 281 2,889 0 15 Sep 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 284 5,835 0 08 Jul 2016