Pierre Stock

According to our database, Pierre Stock authored at least 21 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Mixtral of Experts.

Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de Las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed

CoRR, 2024

2023

Reconciling Security and Communication Efficiency in Federated Learning.

Karthik Prasad, Sayan Ghosh, Graham Cormode, Ilya Mironov, Ashkan Yousefpour, Pierre Stock

IEEE Data Eng. Bull., 2023

Mistral 7B.

Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de Las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed

CoRR, 2023

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

Zechun Liu, Barlas Oguz, Changsheng Zhao, Ernie Chang, Pierre Stock, Yashar Mehdad, Yangyang Shi, Raghuraman Krishnamoorthi, Vikas Chandra

CoRR, 2023

EXACT: Extensive Attack for Split Learning.

Xinchi Qiu, Ilias Leontiadis, Luca Melis, Alexandre Sablayrolles, Pierre Stock

CoRR, 2023

Green Federated Learning.

Ashkan Yousefpour, Shen Guo, Ashish Shenoy, Sayan Ghosh, Pierre Stock, Kiwan Maeng, Schalk-Willem Krüger, Michael G. Rabbat, Carole-Jean Wu, Ilya Mironov

CoRR, 2023

TAN Without a Burn: Scaling Laws of DP-SGD.

Tom Sander, Pierre Stock, Alexandre Sablayrolles

Proceedings of the International Conference on Machine Learning, 2023

Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design.

Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Michael G. Rabbat

Proceedings of the International Conference on Machine Learning, 2023

CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning.

Samuel Maddock, Alexandre Sablayrolles, Pierre Stock

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

The Interpolated MVU Mechanism For Communication-efficient Private Federated Learning.

Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Mike Rabbat

CoRR, 2022

Defending against Reconstruction Attacks with Rényi Differential Privacy.

Pierre Stock, Igor Shilov, Ilya Mironov, Alexandre Sablayrolles

CoRR, 2022

2021

Efficiency and Redundancy in Deep Learning Models: Theoretical Considerations and Practical Applications. (Efficience et redondance dans les modèles d'apprentissage profond : considérations théoriques et applications pratiques).

Pierre Stock

PhD thesis, 2021

An Embedding of ReLU Networks and an Analysis of their Identifiability.

Pierre Stock, Rémi Gribonval

CoRR, 2021

Training with Quantization Noise for Extreme Model Compression.

Pierre Stock, Angela Fan, Benjamin Graham, Edouard Grave, Rémi Gribonval, Hervé Jégou, Armand Joulin

Proceedings of the 9th International Conference on Learning Representations, 2021

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

Benjamin Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Low Bandwidth Video-Chat Compression Using Deep Generative Models.

Maxime Oquab, Pierre Stock, Daniel Haziza, Tao Xu, Peizhao Zhang, Onur Celebi, Yana Hasson, Patrick Labatut, Bobo Bose-Kolanu, Thibault Peyronel, Camille Couprie

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

2020

Low Bandwidth Video-Chat Compression using Deep Generative Models.

Maxime Oquab, Pierre Stock, Oran Gafni, Daniel Haziza, Tao Xu, Peizhao Zhang, Onur Celebi, Yana Hasson, Patrick Labatut, Bobo Bose-Kolanu, Thibault Peyronel, Camille Couprie

CoRR, 2020

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

Pierre Stock, Armand Joulin, Rémi Gribonval, Benjamin Graham, Hervé Jégou

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Equi-normalization of Neural Networks.

Pierre Stock, Benjamin Graham, Rémi Gribonval, Hervé Jégou

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases.

Pierre Stock, Moustapha Cissé

Proceedings of the Computer Vision - ECCV 2018, 2018

2017

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

Pierre Stock, Moustapha Cissé

CoRR, 2017