Pierre Stock | DeepAI

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Featured Co-authors

Kamalika Chaudhuri
74 publications
Armand Joulin
70 publications
Hervé Jégou
59 publications
Rémi Gribonval
58 publications
Edouard Grave
50 publications
Angela Fan
45 publications
Vikas Chandra
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Chuan Guo
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Matthijs Douze
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Tao Xu
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Yashar Mehdad
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∙ 05/29/2023

LLM-QAT: Data-Free Quantization Aware Training for Large Language Models

Several post-training quantization methods have been applied to large la...

0 Zechun Liu, et al. ∙

∙ 05/22/2023

EXACT: Extensive Attack for Split Learning

Privacy-Preserving machine learning (PPML) can help us train and deploy ...

0 Xinchi Qiu, et al. ∙

∙ 11/08/2022

The Interpolated MVU Mechanism For Communication-efficient Private Federated Learning

We consider private federated learning (FL), where a server aggregates d...

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. ∙

∙ 07/20/2021

An Embedding of ReLU Networks and an Analysis of their Identifiability

Neural networks with the Rectified Linear Unit (ReLU) nonlinearity are d...

0 Pierre Stock, et al. ∙

∙ 04/02/2021

LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference

We design a family of image classification architectures that optimize t...

0 Ben Graham, et al. ∙

∙ 12/01/2020

Low Bandwidth Video-Chat Compression using Deep Generative Models

To unlock video chat for hundreds of millions of people hindered by poor...

2 Maxime Oquab, et al. ∙

∙ 04/15/2020

Training with Quantization Noise for Extreme Model Compression

We tackle the problem of producing compact models, maximizing their accu...

30 Angela Fan, et al. ∙

∙ 04/15/2020

Training with Quantization Noise for Extreme Fixed-Point Compression

We tackle the problem of producing compact models, maximizing their accu...

3 Angela Fan, et al. ∙

∙ 07/12/2019

And the Bit Goes Down: Revisiting the Quantization of Neural Networks

In this paper, we address the problem of reducing the memory footprint o...

1 Pierre Stock, et al. ∙

∙ 02/27/2019

Equi-normalization of Neural Networks

Modern neural networks are over-parametrized. In particular, each rectif...

4 Pierre Stock, et al. ∙

∙ 11/30/2017

ConvNets and ImageNet Beyond Accuracy: Explanations, Bias Detection, Adversarial Examples and Model Criticism

ConvNets and Imagenet have driven the recent success of deep learning fo...

0 Pierre Stock, et al. ∙

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