139 citations · 308 across the 8 of their papers we have counts for
6 papers · 1 filter
Identifying Statistical Bias in Dataset Replication
Logan Engstrom, Andrew Ilyas, Shibani Santurkar +3
Dataset replication is a useful tool for assessing whether improvements in test accuracy on a specific benchmark correspond to improvements in models' ability to generalize reliabl…
Adversarial Robustness as a Prior for Learned Representations
Logan Engstrom, Andrew Ilyas, Shibani Santurkar +3
An important goal in deep learning is to learn versatile, high-level feature representations of input data. However, standard networks' representations seem to possess shortcomings…
Adversarial Examples Are Not Bugs, They Are Features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras +3
Adversarial examples have attracted significant attention in machine learning, but the reasons for their existence and pervasiveness remain unclear. We demonstrate that adversarial…
Evaluating and Understanding the Robustness of Adversarial Logit Pairing
Logan Engstrom, Andrew Ilyas, Anish Athalye
We evaluate the robustness of Adversarial Logit Pairing, a recently proposed defense against adversarial examples. We find that a network trained with Adversarial Logit Pairing ach…
Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors
Andrew Ilyas, Logan Engstrom, Aleksander Madry
We study the problem of generating adversarial examples in a black-box setting in which only loss-oracle access to a model is available. We introduce a framework that conceptually…
How Does Batch Normalization Help Optimization?
Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas +1
Batch Normalization (BatchNorm) is a widely adopted technique that enables faster and more stable training of deep neural networks (DNNs). Despite its pervasiveness, the exact reas…