research
∙
06/06/2023
Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings
Research on adversarial robustness is primarily focused on image and tex...
share
research
∙
05/25/2023
Sharpness-Aware Minimization Leads to Low-Rank Features
Sharpness-aware minimization (SAM) is a recently proposed method that mi...
research
∙
02/14/2023
A modern look at the relationship between sharpness and generalization
Sharpness of minima is a promising quantity that can correlate with gene...
research
∙
10/11/2022
SGD with large step sizes learns sparse features
We showcase important features of the dynamics of the Stochastic Gradien...
research
∙
06/13/2022
Towards Understanding Sharpness-Aware Minimization
Sharpness-Aware Minimization (SAM) is a recent training method that reli...
research
∙
02/25/2022
ARIA: Adversarially Robust Image Attribution for Content Provenance
Image attribution – matching an image back to a trusted source – is an e...
research
∙
03/03/2021
On the effectiveness of adversarial training against common corruptions
The literature on robustness towards common corruptions shows no consens...
research
∙
10/19/2020
RobustBench: a standardized adversarial robustness benchmark
Evaluation of adversarial robustness is often error-prone leading to ove...
research
∙
07/06/2020
Understanding and Improving Fast Adversarial Training
A recent line of work focused on making adversarial training computation...
research
∙
06/23/2020
Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks
A large body of research has focused on adversarial attacks which requir...
research
∙
06/08/2020
On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines
Fine-tuning pre-trained transformer-based language models such as BERT h...
research
∙
11/29/2019
Square Attack: a query-efficient black-box adversarial attack via random search
We propose the Square Attack, a new score-based black-box l_2 and l_∞ ad...
research
∙
06/08/2019
Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks
The problem of adversarial samples has been studied extensively for neur...
research
∙
12/13/2018
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem
Classifiers used in the wild, in particular for safety-critical systems,...
research
∙
10/29/2018
Logit Pairing Methods Can Fool Gradient-Based Attacks
Recently, several logit regularization methods have been proposed in [Ka...
research
∙
10/17/2018
Provable Robustness of ReLU networks via Maximization of Linear Regions
It has been shown that neural network classifiers are not robust. This r...
research
∙
05/23/2017