ConvNets and ImageNet Beyond Accuracy: Explanations, Bias Detection, Adversarial Examples and Model Criticism. - Pierre Stock; Moustapha Cissé (CoRR, 2017)

ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases. - Pierre Stock; Moustapha Cissé (ECCV (6), 2018)

Equi-normalization of Neural Networks. - Pierre Stock; Benjamin Graham; Rémi Gribonval; Hervé Jégou (ICLR (Poster), 2019)

And the Bit Goes Down: Revisiting the Quantization of Neural Networks. - Pierre Stock; Armand Joulin; Rémi Gribonval; Benjamin Graham; Hervé Jégou (ICLR, 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)

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 (CVPR Workshops, 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 (ICCV, 2021)

Training with Quantization Noise for Extreme Model Compression. - Pierre Stock; Angela Fan; Benjamin Graham; Edouard Grave; Rémi Gribonval; Hervé Jégou; Armand Joulin (ICLR, 2021)

An Embedding of ReLU Networks and an Analysis of their Identifiability. - Pierre Stock; Rémi Gribonval (CoRR, 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 (2021)

Defending against Reconstruction Attacks with Rényi Differential Privacy. - Pierre Stock; Igor Shilov; Ilya Mironov; Alexandre Sablayrolles (CoRR, 2022)

The Interpolated MVU Mechanism For Communication-efficient Private Federated Learning. - Chuan Guo 001; Kamalika Chaudhuri; Pierre Stock; Mike Rabbat (CoRR, 2022)

CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning. - Samuel Maddock; Alexandre Sablayrolles; Pierre Stock (ICLR, 2023)

Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design. - Chuan Guo 001; Kamalika Chaudhuri; Pierre Stock; Michael G. Rabbat (ICML, 2023)

TAN Without a Burn: Scaling Laws of DP-SGD. - Tom Sander; Pierre Stock; Alexandre Sablayrolles (ICML, 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)

EXACT: Extensive Attack for Split Learning. - Xinchi Qiu; Ilias Leontiadis; Luca Melis; Alexandre Sablayrolles; Pierre Stock (CoRR, 2023)

Mistral 7B. - Albert Q. Jiang; Alexandre Sablayrolles; Arthur Mensch; Chris Bamford 001; Devendra Singh Chaplot; Diego de Las Casas; Florian Bressand; Gianna Lengyel; Guillaume Lample; Lucile Saulnier; Lélio Renard Lavaud; Marie-Anne Lachaux; et al. (CoRR, 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)

LLM-QAT: Data-Free Quantization Aware Training for Large Language Models. - Zechun Liu; Barlas Oguz; Changsheng Zhao 002; Ernie Chang; Pierre Stock; Yashar Mehdad; Yangyang Shi; Raghuraman Krishnamoorthi; Vikas Chandra (ACL (Findings), 2024)

Mixtral of Experts. - Albert Q. Jiang; Alexandre Sablayrolles; Antoine Roux; Arthur Mensch; Blanche Savary; Chris Bamford 001; Devendra Singh Chaplot; Diego de Las Casas; Emma Bou Hanna; Florian Bressand; Gianna Lengyel; Guillaume Bour; et al. (CoRR, 2024)

Pixtral 12B. - Pravesh Agrawal; Szymon Antoniak; Emma Bou Hanna; Baptiste Bout; Devendra Singh Chaplot; Jessica Chudnovsky; Diogo Costa; Baudouin De Monicault; Saurabh Garg; Théophile Gervet; Soham Ghosh; Amélie Héliou; et al. (CoRR, 2024)

Magistral. - Abhinav Rastogi; Albert Q. Jiang; Andy Lo; Gabrielle Berrada; Guillaume Lample; Jason Rute; Joep Barmentlo; Karmesh Yadav; Kartik Khandelwal; Khyathi Raghavi Chandu; Léonard Blier; Lucile Saulnier; et al. (CoRR, 2025)

Voxtral. - Alexander H. Liu; Andy Ehrenberg; Andy Lo; Clément Denoix; Corentin Barreau; Guillaume Lample; Jean-Malo Delignon; Khyathi Raghavi Chandu; Patrick von Platen; Pavankumar Reddy Muddireddy; Sanchit Gandhi; Soham Ghosh; et al. (CoRR, 2025)