Alexandre Sablayrolles | DeepAI

AI Chat

Featured Co-authors

Yue Wang
263 publications
Cordelia Schmid
156 publications
Matthieu Cord
95 publications
Gabriel Synnaeve
73 publications
Douwe Kiela
70 publications
Hervé Jégou
59 publications
Marc'Aurelio Ranzato
47 publications
Chuan Guo
42 publications
Nicolas Usunier
41 publications
Matthijs Douze
38 publications
Ludovic Denoyer
37 publications

∙ 06/07/2023

Privately generating tabular data using language models

Privately generating synthetic data from a table is an important brick o...

0 Alexandre Sablayrolles, et al. ∙

∙ 10/24/2022

Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano

Differential privacy (DP) is by far the most widely accepted framework f...

0 Chuan Guo, et al. ∙

∙ 10/07/2022

TAN without a burn: Scaling Laws of DP-SGD

Differentially Private methods for training Deep Neural Networks (DNNs) ...

0 Tom Sander, et al. ∙

∙ 10/06/2022

CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning

Federated Learning (FL) is a setting for training machine learning model...

0 Samuel Maddock, et al. ∙

∙ 02/15/2022

Defending against Reconstruction Attacks with Rényi Differential Privacy

Reconstruction attacks allow an adversary to regenerate data samples of ...

0 Pierre Stock, et al. ∙

∙ 12/17/2021

Watermarking Images in Self-Supervised Latent Spaces

We revisit watermarking techniques based on pre-trained deep networks, i...

0 Pierre Fernandez, et al. ∙

∙ 12/17/2021

Nearest neighbor search with compact codes: A decoder perspective

Modern approaches for fast retrieval of similar vectors on billion-scale...

0 Kenza Amara, et al. ∙

∙ 09/25/2021

Opacus: User-Friendly Differential Privacy Library in PyTorch

We introduce Opacus, a free, open-source PyTorch library for training de...

0 Ashkan Yousefpour, et al. ∙

∙ 04/15/2021

Gradient-based Adversarial Attacks against Text Transformers

We propose the first general-purpose gradient-based attack against trans...

0 Chuan Guo, et al. ∙

∙ 03/31/2021

Going deeper with Image Transformers

Transformers have been recently adapted for large scale image classifica...

0 Hugo Touvron, et al. ∙

∙ 12/23/2020

Training data-efficient image transformers distillation through attention

Recently, neural networks purely based on attention were shown to addres...

15 Hugo Touvron, et al. ∙

∙ 11/25/2020

Grafit: Learning fine-grained image representations with coarse labels

This paper tackles the problem of learning a finer representation than t...

0 Hugo Touvron, et al. ∙

∙ 02/03/2020

Radioactive data: tracing through training

We want to detect whether a particular image dataset has been used to tr...

2 Alexandre Sablayrolles, et al. ∙

∙ 08/29/2019

White-box vs Black-box: Bayes Optimal Strategies for Membership Inference

Membership inference determines, given a sample and trained parameters o...

12 Alexandre Sablayrolles, et al. ∙

∙ 07/10/2019

Large Memory Layers with Product Keys

This paper introduces a structured memory which can be easily integrated...

0 Guillaume Lample, et al. ∙

∙ 09/17/2018

Déjà Vu: an empirical evaluation of the memorization properties of ConvNets

Convolutional neural networks memorize part of their training data, whic...

0 Alexandre Sablayrolles, et al. ∙

∙ 06/08/2018

A neural network catalyzer for multi-dimensional similarity search

This paper aims at learning a function mapping input vectors to an outpu...

0 Alexandre Sablayrolles, et al. ∙

∙ 04/26/2018

Link and code: Fast indexing with graphs and compact regression codes

Similarity search approaches based on graph walks have recently attained...

0 MMatthijs Douze, et al. ∙

∙ 09/21/2016

How should we evaluate supervised hashing?

Hashing produces compact representations for documents, to perform tasks...

0 Alexandre Sablayrolles, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world