159 citations · 509 across the 29 of their papers we have counts for
4 papers · 1 filter
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…
Deep k-NN Defense against Clean-label Data Poisoning Attacks
Neehar Peri, Neal Gupta, W. Ronny Huang +5
Targeted clean-label data poisoning is a type of adversarial attack on machine learning systems in which an adversary injects a few correctly-labeled, minimally-perturbed samples i…
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…