activity
20162024
most citedConvNets Match Vision Transformers at Scale

16 citations · 46 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI20242 cited

Operationalizing Contextual Integrity in Privacy-Conscious Assistants

Sahra Ghalebikesabi, Eugene Bagdasaryan, Ren Yi +10

Advanced AI assistants combine frontier LLMs and tool access to autonomously perform complex tasks on behalf of users. While the helpfulness of such assistants can increase dramati…

cs.CV202316 cited

ConvNets Match Vision Transformers at Scale

Samuel L. Smith, Andrew Brock, Leonard Berrada +1

Many researchers believe that ConvNets perform well on small or moderately sized datasets, but are not competitive with Vision Transformers when given access to datasets on the web…

cs.LG20235 cited

Unlocking Accuracy and Fairness in Differentially Private Image Classification

Leonard Berrada, Soham De, Judy Hanwen Shen +6

Privacy-preserving machine learning aims to train models on private data without leaking sensitive information. Differential privacy (DP) is considered the gold standard framework…

cs.LG202315 cited

Differentially Private Diffusion Models Generate Useful Synthetic Images

Sahra Ghalebikesabi, Leonard Berrada, Sven Gowal +7

The ability to generate privacy-preserving synthetic versions of sensitive image datasets could unlock numerous ML applications currently constrained by data availability. Due to t…

cs.LG20168 cited

Trusting SVM for Piecewise Linear CNNs

Leonard Berrada, Andrew Zisserman, M. Pawan Kumar

We present a novel layerwise optimization algorithm for the learning objective of Piecewise-Linear Convolutional Neural Networks (PL-CNNs), a large class of convolutional neural ne…