| Mistral 7B AQ Jiang, A Sablayrolles, A Mensch, C Bamford, DS Chaplot, D Casas, ... arXiv preprint arXiv:2310.06825, 2023 | 2689* | 2023 |
| 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 | 788* | 2021 |
| Mixtral of experts AQ Jiang, A Sablayrolles, A Roux, A Mensch, B Savary, C Bamford, ... arXiv preprint arXiv:2401.04088, 2024 | 740 | 2024 |
| 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 | 254* | 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 | 253* | 2018 |
| 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 | 171 | 2019 |
| 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 | 142 | 2023 |
| 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 | 43 | 2020 |
| TAN without a burn: Scaling Laws of DP-SGD T Sander, P Stock, A Sablayrolles International Conference on Machine Learning (ICML 2023), 2022 | 36 | 2022 |
| Defending against Reconstruction Attacks with Rényi Differential Privacy P Stock, I Shilov, I Mironov, A Sablayrolles arXiv preprint arXiv:2202.07623, 2022 | 30 | 2022 |
| Green federated learning A Yousefpour, S Guo, A Shenoy, S Ghosh, P Stock, K Maeng, SW Krüger, ... arXiv preprint arXiv:2303.14604, 2023 | 20 | 2023 |
| CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning S Maddock, A Sablayrolles, P Stock International Conference on Learning Representations (ICLR 2023), 2022 | 17 | 2022 |
| An Embedding of ReLU Networks and an Analysis of their Identifiability P Stock, R Gribonval Constructive Approximation (2022), 2021 | 14 | 2021 |
| Efficiency and Redundancy in Deep Learning Models: Theoretical Considerations and Practical Applications P Stock École Normale Supérieure de Lyon (2021), 2021 | 9 | 2021 |
| 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 | 8* | 2023 |
| 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 | 8 | 2022 |
| EXACT: Extensive Attack for Split Learning X Qiu, I Leontiadis, L Melis, A Sablayrolles, P Stock arXiv preprint arXiv:2305.12997, 2023 | 4 | 2023 |
| Evaluating Privacy Leakage in Split Learning X Qiu, I Leontiadis, L Melis, A Sablayrolles, P Stock arXiv preprint arXiv:2305.12997, 2023 | 1 | 2023 |
| 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 | 1 | 2022 |
| Equi-normalization of Neural Networks P Stock, B Graham, R Gribonval, H Jégou International Conference on Learning Representations (ICLR 2019), 2019 | | 2019 |
Alexandre SablayrollesMeta AIAdresse e-mail validée de meta.com
