On the Effect of Low-Rank Weights on Adversarial Robustness of Neural
Networks

v1v2 (latest)

On the Effect of Low-Rank Weights on Adversarial Robustness of Neural Networks

29 January 2019

ArXiv (abs)PDF HTML

Papers citing "On the Effect of Low-Rank Weights on Adversarial Robustness of Neural Networks"

10 / 10 papers shown

Title
When Can We Solve the Weighted Low Rank Approximation Problem in Truly Subquadratic Time? Chenyang Li Yingyu Liang Zhenmei Shi Zhao Song 74 5 0 24 Feb 2025
On Fairness of Low-Rank Adaptation of Large Models Zhoujie Ding Ken Ziyu Liu Pura Peetathawatchai Berivan Isik Sanmi Koyejo 81 5 0 27 May 2024
Revisiting the Trade-off between Accuracy and Robustness via Weight Distribution of Filters Xingxing Wei Shiji Zhao Bo li AAML 112 7 0 06 Jun 2023
Robust low-rank training via approximate orthonormal constraints Dayana Savostianova Emanuele Zangrando Gianluca Ceruti Francesco Tudisco 69 10 0 02 Jun 2023
Transformed Low-Rank Parameterization Can Help Robust Generalization for Tensor Neural Networks Andong Wang Chong Li Mingyuan Bai Zhong Jin Guoxu Zhou Qianchuan Zhao OOD AAML 34 5 0 01 Mar 2023
Generating Structured Adversarial Attacks Using Frank-Wolfe Method Ehsan Kazemi Thomas Kerdreux Liquang Wang AAML DiffM 48 1 0 15 Feb 2021
Linking average- and worst-case perturbation robustness via class selectivity and dimensionality Matthew L. Leavitt Ari S. Morcos AAML 48 2 0 14 Oct 2020
Pareto Probing: Trading Off Accuracy for Complexity Tiago Pimentel Naomi Saphra Adina Williams Ryan Cotterell 89 60 0 05 Oct 2020
Trace-Norm Adversarial Examples Ehsan Kazemi Thomas Kerdreux Liqiang Wang 59 2 0 02 Jul 2020
Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory Micah Goldblum Jonas Geiping Avi Schwarzschild Michael Moeller Tom Goldstein 103 34 0 01 Oct 2019