| Convolutional sequence to sequence learning J Gehring, M Auli, D Grangier, D Yarats, YN Dauphin International conference on machine learning, 1243-1252, 2017 | 3768 | 2017 |
| wav2vec 2.0: A framework for self-supervised learning of speech representations A Baevski, Y Zhou, A Mohamed, M Auli Advances in neural information processing systems 33, 12449-12460, 2020 | 3400 | 2020 |
| Somatic mutations affect key pathways in lung adenocarcinoma L Ding, G Getz, DA Wheeler, ER Mardis, MD McLellan, K Cibulskis, ... Nature 455 (7216), 1069-1075, 2008 | 3030 | 2008 |
| fairseq: A fast, extensible toolkit for sequence modeling M Ott, S Edunov, A Baevski, A Fan, S Gross, N Ng, D Grangier, M Auli arXiv preprint arXiv:1904.01038, 2019 | 2635 | 2019 |
| Language modeling with gated convolutional networks YN Dauphin, A Fan, M Auli, D Grangier International conference on machine learning, 933-941, 2017 | 2304 | 2017 |
| Sequence level training with recurrent neural networks MA Ranzato, S Chopra, M Auli, W Zaremba arXiv preprint arXiv:1511.06732, 2015 | 1707 | 2015 |
| wav2vec: Unsupervised pre-training for speech recognition S Schneider, A Baevski, R Collobert, M Auli arXiv preprint arXiv:1904.05862, 2019 | 1205 | 2019 |
| Understanding back-translation at scale S Edunov, M Ott, M Auli, D Grangier arXiv preprint arXiv:1808.09381, 2018 | 1114 | 2018 |
| Abstractive sentence summarization with attentive recurrent neural networks S Chopra, M Auli, AM Rush Proceedings of the 2016 conference of the North American chapter of the …, 2016 | 1090 | 2016 |
| A neural network approach to context-sensitive generation of conversational responses A Sordoni, M Galley, M Auli, C Brockett, Y Ji, M Mitchell, JY Nie, J Gao, ... arXiv preprint arXiv:1506.06714, 2015 | 1090 | 2015 |
| 3d human pose estimation in video with temporal convolutions and semi-supervised training D Pavllo, C Feichtenhofer, D Grangier, M Auli Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 962 | 2019 |
| Analyzing uncertainty in neural machine translation M Ott, M Auli, D Grangier, MA Ranzato International Conference on Machine Learning, 3956-3965, 2018 | 814 | 2018 |
| Wizard of wikipedia: Knowledge-powered conversational agents E Dinan, S Roller, K Shuster, A Fan, M Auli, J Weston arXiv preprint arXiv:1811.01241, 2018 | 785 | 2018 |
| Pay less attention with lightweight and dynamic convolutions F Wu, A Fan, A Baevski, YN Dauphin, M Auli arXiv preprint arXiv:1901.10430, 2019 | 596 | 2019 |
| vq-wav2vec: Self-supervised learning of discrete speech representations A Baevski, S Schneider, M Auli arXiv preprint arXiv:1910.05453, 2019 | 562 | 2019 |
| A convolutional encoder model for neural machine translation J Gehring, M Auli, D Grangier, YN Dauphin arXiv preprint arXiv:1611.02344, 2016 | 542 | 2016 |
| Unsupervised cross-lingual representation learning for speech recognition A Conneau, A Baevski, R Collobert, A Mohamed, M Auli arXiv preprint arXiv:2006.13979, 2020 | 522 | 2020 |
| Data2vec: A general framework for self-supervised learning in speech, vision and language A Baevski, WN Hsu, Q Xu, A Babu, J Gu, M Auli International Conference on Machine Learning, 1298-1312, 2022 | 497 | 2022 |
| Neural text generation from structured data with application to the biography domain R Lebret, D Grangier, M Auli arXiv preprint arXiv:1603.07771, 2016 | 491 | 2016 |
| Beyond english-centric multilingual machine translation A Fan, S Bhosale, H Schwenk, Z Ma, A El-Kishky, S Goyal, M Baines, ... The Journal of Machine Learning Research 22 (1), 4839-4886, 2021 | 488 | 2021 |
