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Pierre Stock
Pierre Stock
Mistral AI
Dirección de correo verificada de mistral.ai
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Año
Mistral 7B
AQ Jiang, A Sablayrolles, A Mensch, C Bamford, DS Chaplot
Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio …, 2023
5760*2023
Mixtral of experts
AQ Jiang, A Sablayrolles, A Roux, A Mensch, B Savary, C Bamford, ...
arXiv preprint arXiv:2401.04088, 2024
15812024
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
981*2021
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
280*2020
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
276*2018
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
2562023
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
1862019
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
562020
Pixtral 12B
P Agrawal, S Antoniak, EB Hanna, B Bout, D Chaplot, J Chudnovsky, ...
arXiv preprint arXiv:2410.07073, 2024
542024
TAN without a burn: Scaling Laws of DP-SGD
T Sander, P Stock, A Sablayrolles
International Conference on Machine Learning (ICML 2023), 2022
452022
Defending against Reconstruction Attacks with Rényi Differential Privacy
P Stock, I Shilov, I Mironov, A Sablayrolles
arXiv preprint arXiv:2202.07623, 2022
382022
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning
S Maddock, A Sablayrolles, P Stock
International Conference on Learning Representations (ICLR 2023), 2022
232022
Green federated learning
A Yousefpour, S Guo, A Shenoy, S Ghosh, P Stock, K Maeng, SW Krüger, ...
arXiv preprint arXiv:2303.14604, 2023
202023
An Embedding of ReLU Networks and an Analysis of their Identifiability
P Stock, R Gribonval
Constructive Approximation (2022), 2021
202021
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design
C Guo, K Chaudhuri, P Stock, M Rabbat
International Conference on Machine Learning (ICML 2023), 2023
9*2023
Efficiency and Redundancy in Deep Learning Models: Theoretical Considerations and Practical Applications
P Stock
École Normale Supérieure de Lyon (2021), 2021
92021
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
72022
Exact: Extensive attack for split learning
X Qiu, I Leontiadis, L Melis, A Sablayrolles, P Stock
arXiv preprint arXiv:2305.12997, 2023
42023
Evaluating Privacy Leakage in Split Learning
X Qiu, I Leontiadis, L Melis, A Sablayrolles, P Stock
arXiv preprint arXiv:2305.12997, 2023
22023
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
2022
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