19 citations · 100 across the 10 of their papers we have counts for
10 papers
Rethinking Backdoor Attacks
Alaa Khaddaj, Guillaume Leclerc, Aleksandar Makelov +4
In a backdoor attack, an adversary inserts maliciously constructed backdoor examples into a training set to make the resulting model vulnerable to manipulation. Defending against s…
FFCV: Accelerating Training by Removing Data Bottlenecks
Guillaume Leclerc, Andrew Ilyas, Logan Engstrom +3
We present FFCV, a library for easy and fast machine learning model training. FFCV speeds up model training by eliminating (often subtle) data bottlenecks from the training process…
TRAK: Attributing Model Behavior at Scale
Sung Min Park, Kristian Georgiev, Andrew Ilyas +2
The goal of data attribution is to trace model predictions back to training data. Despite a long line of work towards this goal, existing approaches to data attribution tend to for…
Raising the Cost of Malicious AI-Powered Image Editing
Hadi Salman, Alaa Khaddaj, Guillaume Leclerc +2
We present an approach to mitigating the risks of malicious image editing posed by large diffusion models. The key idea is to immunize images so as to make them resistant to manipu…
Adversarially trained neural representations may already be as robust as corresponding biological neural representations
Chong Guo, Michael J. Lee, Guillaume Leclerc +4
Visual systems of primates are the gold standard of robust perception. There is thus a general belief that mimicking the neural representations that underlie those systems will yie…
Datamodels: Predicting Predictions from Training Data
Andrew Ilyas, Sung Min Park, Logan Engstrom +2
We present a conceptual framework, datamodeling, for analyzing the behavior of a model class in terms of the training data. For any fixed "target" example , training set , an…
3DB: A Framework for Debugging Computer Vision Models
Guillaume Leclerc, Hadi Salman, Andrew Ilyas +9
We introduce 3DB: an extendable, unified framework for testing and debugging vision models using photorealistic simulation. We demonstrate, through a wide range of use cases, that…
Revisiting Ensembles in an Adversarial Context: Improving Natural Accuracy
Aditya Saligrama, Guillaume Leclerc
A necessary characteristic for the deployment of deep learning models in real world applications is resistance to small adversarial perturbations while maintaining accuracy on non-…
The Two Regimes of Deep Network Training
Guillaume Leclerc, Aleksander Madry
Learning rate schedule has a major impact on the performance of deep learning models. Still, the choice of a schedule is often heuristical. We aim to develop a precise understandin…
Smallify: Learning Network Size while Training
Guillaume Leclerc, Manasi Vartak, Raul Castro Fernandez +2
As neural networks become widely deployed in different applications and on different hardware, it has become increasingly important to optimize inference time and model size along…