164 citations · 1.3k across the 73 of their papers we have counts for
5 papers · 1 filter
WITCHcraft: Efficient PGD attacks with random step size
Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum +4
State-of-the-art adversarial attacks on neural networks use expensive iterative methods and numerous random restarts from different initial points. Iterative FGSM-based methods wit…
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach
Micah Goldblum, Liam Fowl, Tom Goldstein
Previous work on adversarially robust neural networks for image classification requires large training sets and computationally expensive training procedures. On the other hand, fe…
Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory
Micah Goldblum, Jonas Geiping, Avi Schwarzschild +2
We empirically evaluate common assumptions about neural networks that are widely held by practitioners and theorists alike. In this work, we: (1) prove the widespread existence of…
Understanding Generalization through Visualizations
W. Ronny Huang, Zeyad Emam, Micah Goldblum +4
The power of neural networks lies in their ability to generalize to unseen data, yet the underlying reasons for this phenomenon remain elusive. Numerous rigorous attempts have been…
Adversarially Robust Distillation
Micah Goldblum, Liam Fowl, Soheil Feizi +1
Knowledge distillation is effective for producing small, high-performance neural networks for classification, but these small networks are vulnerable to adversarial attacks. This p…