Pierre Stock
Pierre Stock
Meta AI
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LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
B Graham, A El-Nouby, H Touvron, P Stock, A Joulin, H Jégou, M Douze
International Conference on Computer Vision (ICCV 2021), 2021
Convnets and ImageNet Beyond Accuracy: Explanations, Bias Detection, Adversarial Examples and Model Criticism
P Stock, M Cisse
European Conference on Computer Vision (ECCV 2018), 2018
Training with Quantization Noise for Extreme Model Compression
P Stock*, A Fan*, B Graham, E Grave, R Gribonval, H Jegou, A Joulin
International Conference on Learning Representations (ICLR 2021), 2020
And the Bit Goes Down: Revisiting the Quantization of Neural Networks
P Stock, A Joulin, R Gribonval, B Graham, H Jégou
International Conference on Learning Representations (ICLR 2020), 2019
Low Bandwidth Video-Chat Compression using Deep Generative Models
M Oquab*, P Stock*, O Gafni, D Haziza, T Xu, P Zhang, O Celebi, ...
Mobile AI Workshop (MAI CVPR 2021), 2020
Defending against Reconstruction Attacks with Rényi Differential Privacy
P Stock, I Shilov, I Mironov, A Sablayrolles
arXiv preprint arXiv:2202.07623, 2022
TAN without a burn: Scaling Laws of DP-SGD
T Sander, P Stock, A Sablayrolles
International Conference on Machine Learning (ICML 2023), 2022
An Embedding of ReLU Networks and an Analysis of their Identifiability
P Stock, R Gribonval
Constructive Approximation (2022), 2021
Efficiency and Redundancy in Deep Learning Models: Theoretical Considerations and Practical Applications
P Stock
École Normale Supérieure de Lyon (2021), 2021
Reconciling Security and Communication Efficiency in Federated Learning
K Prasad, S Ghosh, G Cormode, I Mironov, A Yousefpour, P Stock
Workshop on Federated Learning (FL-NeurIPS 2022), 2022
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning
S Maddock, A Sablayrolles, P Stock
International Conference on Learning Representations (ICLR 2023), 2022
Systems and Methods for Low Bandwidth Video-Chat Compression
MM Oquab, P Stock, O Gafni, DRD Haziza, T Xu, P Zhang, O Çelebi, ...
US Patent App. 17/224,103, 2022
The Interpolated MVU Mechanism For Communication-efficient Private Federated Learning
C Guo, K Chaudhuri, P Stock, M Rabbat
Workshop on Federated Learning (FL-NeurIPS 2022), 2022
LLM-QAT: Data-Free Quantization Aware Training for Large Language Models
Z Liu, B Oguz, C Zhao, E Chang, P Stock, Y Mehdad, Y Shi, ...
arXiv preprint arXiv:2305.17888, 2023
EXACT: Extensive Attack for Split Learning
X Qiu, I Leontiadis, L Melis, A Sablayrolles, P Stock
arXiv preprint arXiv:2305.12997, 2023
Green Federated Learning
A Yousefpour, S Guo, A Shenoy, S Ghosh, P Stock, K Maeng, SW Krüger, ...
arXiv preprint arXiv:2303.14604, 2023
Equi-normalization of Neural Networks
P Stock, B Graham, R Gribonval, H Jégou
International Conference on Learning Representations (ICLR 2019), 2019
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