Search Results for author:
Found 6 papers, 5 papers with code
Adversarial Examples Are Not Bugs, They Are Features
Adversarial examples have attracted significant attention in machine learning, but the reasons for their existence and pervasiveness remain unclear.
Adversarial Robustness as a Prior for Learned Representations
In this work, we show that robust optimization can be re-cast as a tool for enforcing priors on the features learned by deep neural networks.
Image Synthesis with a Single (Robust) Classifier
We show that the basic classification framework alone can be used to tackle some of the most challenging tasks in image synthesis.
Ranked #60 on
Image Generation
on CIFAR-10
(Inception score metric)
Exploring the Landscape of Spatial Robustness
The study of adversarial robustness has so far largely focused on perturbations bound in p-norms.
Spectral Signatures in Backdoor Attacks
In this paper, we identify a new property of all known backdoor attacks, which we call \emph{spectral signatures}.
Domain Adaptation for Enterprise Email Search
To address this data challenge, in this paper we propose a domain adaptation approach that fine-tunes the global model to each individual enterprise.
