| Convolutional sequence to sequence learning J Gehring, M Auli, D Grangier, D Yarats, YN Dauphin International Conference on Machine Learning, 1243-1252, 2017 | 2288 | 2017 |
| Language modeling with gated convolutional networks YN Dauphin, A Fan, M Auli, D Grangier International conference on machine learning, 933-941, 2017 | 1161 | 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 | 900 | 2019 |
| Understanding back-translation at scale S Edunov, M Ott, M Auli, D Grangier arXiv preprint arXiv:1808.09381, 2018 | 494 | 2018 |
| Label embedding trees for large multi-class tasks S Bengio, J Weston, D Grangier | 425 | 2010 |
| 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 | 404 | 2008 |
| A convolutional encoder model for neural machine translation J Gehring, M Auli, D Grangier, YN Dauphin arXiv preprint arXiv:1611.02344, 2016 | 338 | 2016 |
| Scaling neural machine translation M Ott, S Edunov, D Grangier, M Auli arXiv preprint arXiv:1806.00187, 2018 | 331 | 2018 |
| 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 | 286 | 2019 |
| Neural text generation from structured data with application to the biography domain R Lebret, D Grangier, M Auli arXiv preprint arXiv:1603.07771, 2016 | 258 | 2016 |
| Efficient softmax approximation for GPUs A Joulin, M Cissé, D Grangier, H Jégou International conference on machine learning, 1302-1310, 2017 | 204 | 2017 |
| Discriminative keyword spotting J Keshet, D Grangier, S Bengio Speech Communication 51 (4), 317-329, 2009 | 165 | 2009 |
| Controllable abstractive summarization A Fan, D Grangier, M Auli arXiv preprint arXiv:1711.05217, 2017 | 150 | 2017 |
| Strategies for training large vocabulary neural language models W Chen, D Grangier, M Auli arXiv preprint arXiv:1512.04906, 2015 | 146 | 2015 |
| 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 | 132 | 2010 |
| 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 | 130 | 2017 |
| Quaternet: A quaternion-based recurrent model for human motion D Pavllo, D Grangier, M Auli arXiv preprint arXiv:1805.06485, 2018 | 112 | 2018 |
| Deep convolutional networks for scene parsing D Grangier, L Bottou, R Collobert ICML 2009 Deep Learning Workshop 3 (6), 109, 2009 | 110 | 2009 |
| Supervised semantic indexing B Bai, J Weston, D Grangier, R Collobert, K Sadamasa, Y Qi, O Chapelle, ... Proceedings of the 18th ACM conference on Information and knowledge …, 2009 | 109 | 2009 |
| Analyzing uncertainty in neural machine translation M Ott, M Auli, D Grangier, MA Ranzato International Conference on Machine Learning, 3956-3965, 2018 | 107 | 2018 |
