| Convolutional sequence to sequence learning J Gehring, M Auli, D Grangier, D Yarats, YN Dauphin International Conference on Machine Learning, 1243-1252, 2017 | 2627 | 2017 |
| Language modeling with gated convolutional networks YN Dauphin, A Fan, M Auli, D Grangier International conference on machine learning, 933-941, 2017 | 1383 | 2017 |
| 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 | 1288 | 2019 |
| Understanding back-translation at scale S Edunov, M Ott, M Auli, D Grangier arXiv preprint arXiv:1808.09381, 2018 | 633 | 2018 |
| Label embedding trees for large multi-class tasks S Bengio, J Weston, D Grangier | 434 | 2010 |
| 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 | 414 | 2019 |
| A discriminative kernel-based approach to rank images from text queries D Grangier, S Bengio IEEE transactions on pattern analysis and machine intelligence 30 (8), 1371-1384, 2008 | 412 | 2008 |
| Scaling neural machine translation M Ott, S Edunov, D Grangier, M Auli arXiv preprint arXiv:1806.00187, 2018 | 410 | 2018 |
| A convolutional encoder model for neural machine translation J Gehring, M Auli, D Grangier, YN Dauphin arXiv preprint arXiv:1611.02344, 2016 | 385 | 2016 |
| Neural text generation from structured data with application to the biography domain R Lebret, D Grangier, M Auli arXiv preprint arXiv:1603.07771, 2016 | 311 | 2016 |
| Efficient softmax approximation for GPUs A Joulin, M Cissé, D Grangier, H Jégou International conference on machine learning, 1302-1310, 2017 | 221 | 2017 |
| Controllable abstractive summarization A Fan, D Grangier, M Auli arXiv preprint arXiv:1711.05217, 2017 | 190 | 2017 |
| Discriminative keyword spotting J Keshet, D Grangier, S Bengio Speech Communication 51 (4), 317-329, 2009 | 168 | 2009 |
| Strategies for training large vocabulary neural language models W Chen, D Grangier, M Auli arXiv preprint arXiv:1512.04906, 2015 | 155 | 2015 |
| Quaternet: A quaternion-based recurrent model for human motion D Pavllo, D Grangier, M Auli arXiv preprint arXiv:1805.06485, 2018 | 145 | 2018 |
| Classical structured prediction losses for sequence to sequence learning S Edunov, M Ott, M Auli, D Grangier, MA Ranzato arXiv preprint arXiv:1711.04956, 2017 | 145 | 2017 |
| Learning to rank with (a lot of) word features B Bai, J Weston, D Grangier, R Collobert, K Sadamasa, Y Qi, O Chapelle, ... Information retrieval 13 (3), 291-314, 2010 | 137 | 2010 |
| Analyzing uncertainty in neural machine translation M Ott, M Auli, D Grangier, MA Ranzato International Conference on Machine Learning, 3956-3965, 2018 | 131 | 2018 |
| Efficient content-based sparse attention with routing transformers A Roy, M Saffar, A Vaswani, D Grangier Transactions of the Association for Computational Linguistics 9, 53-68, 2021 | 121 | 2021 |
| Tagged back-translation I Caswell, C Chelba, D Grangier arXiv preprint arXiv:1906.06442, 2019 | 115 | 2019 |
