| Flow matching for generative modeling Y Lipman, RTQ Chen, H Ben-Hamu, M Nickel, M Le arXiv preprint arXiv:2210.02747, 2022 | 1078 | 2022 |
| Voicebox: Text-guided multilingual universal speech generation at scale M Le, A Vyas, B Shi, B Karrer, L Sari, R Moritz, M Williamson, V Manohar, ... Advances in neural information processing systems 36, 14005-14034, 2023 | 282 | 2023 |
| Revisiting the evaluation of theory of mind through question answering M Le, YL Boureau, M Nickel Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019 | 104 | 2019 |
| Audiobox: Unified audio generation with natural language prompts A Vyas, B Shi, M Le, A Tjandra, YC Wu, B Guo, J Zhang, X Zhang, ... arXiv preprint arXiv:2312.15821, 2023 | 97 | 2023 |
| Inferring concept hierarchies from text corpora via hyperbolic embeddings M Le, S Roller, L Papaxanthos, D Kiela, M Nickel arXiv preprint arXiv:1902.00913, 2019 | 93 | 2019 |
| A parameter-free spatio-temporal pattern mining model to catalog global ocean dynamics JH Faghmous, M Le, M Uluyol, V Kumar, S Chatterjee 2013 IEEE 13th International conference on data mining, 151-160, 2013 | 46 | 2013 |
| Assessment of risk of harm associated with intensive blood pressure management among patients with hypertension who smoke: a secondary analysis of the systolic blood pressure … J Scarpa, E Bruzelius, P Doupe, M Le, J Faghmous, A Baum JAMA network open 2 (3), e190005-e190005, 2019 | 34 | 2019 |
| Generative pre-training for speech with flow matching AH Liu, M Le, A Vyas, B Shi, A Tjandra, WN Hsu arXiv preprint arXiv:2310.16338, 2023 | 33 | 2023 |
| The source-target domain mismatch problem in machine translation J Shen, PJ Chen, M Le, J He, J Gu, M Ott, M Auli, MA Ranzato arXiv preprint arXiv:1909.13151, 2019 | 30 | 2019 |
| Guided flows for generative modeling and decision making Q Zheng, M Le, N Shaul, Y Lipman, A Grover, RTQ Chen arXiv preprint arXiv:2311.13443, 2023 | 29 | 2023 |
| Facebook ai's wat19 myanmar-english translation task submission PJ Chen, J Shen, M Le, V Chaudhary, A El-Kishky, G Wenzek, M Ott, ... arXiv preprint arXiv:1910.06848, 2019 | 29 | 2019 |
| Satellite images and machine learning can identify remote communities to facilitate access to health services E Bruzelius, M Le, A Kenny, J Downey, M Danieletto, A Baum, P Doupe, ... Journal of the American Medical Informatics Association 26 (8-9), 806-812, 2019 | 28 | 2019 |
| Flow matching for generative modeling, 2023 Y Lipman, RTQ Chen, H Ben-Hamu, M Nickel, M Le URL https://arxiv. org/abs/2210.02747, 0 | 27 | |
| Multiple hypothesis object tracking for unsupervised self-learning: An ocean eddy tracking application J Faghmous, M Uluyol, L Styles, M Le, V Mithal, S Boriah, V Kumar Proceedings of the AAAI Conference on Artificial Intelligence 27 (1), 1277-1283, 2013 | 24 | 2013 |
| On kinetic optimal probability paths for generative models N Shaul, RTQ Chen, M Nickel, M Le, Y Lipman International Conference on Machine Learning, 30883-30907, 2023 | 20 | 2023 |
| Learning multivariate Hawkes processes at scale M Nickel, M Le arXiv preprint arXiv:2002.12501, 2020 | 18 | 2020 |
| Flow matching guide and code Y Lipman, M Havasi, P Holderrieth, N Shaul, M Le, B Karrer, RTQ Chen, ... arXiv preprint arXiv:2412.06264, 2024 | 15 | 2024 |
| Partial aborts for transactions via first-class continuations M Le, M Fluet ACM SIGPLAN Notices 50 (9), 230-242, 2015 | 15 | 2015 |
| Practical and effective higher-order optimizations L Bergstrom, M Fluet, M Le, J Reppy, N Sandler ACM SIGPLAN Notices 49 (9), 81-93, 2014 | 13 | 2014 |
| Modeling sparse information diffusion at scale via lazy multivariate hawkes processes M Nickel, M Le Proceedings of the web conference 2021, 706-717, 2021 | 12 | 2021 |
Maximilian NickelResearch Scientist at FAIR, Meta AIAdresă de e-mail confirmată pe meta.com
