| On using very large target vocabulary for neural machine translation S Jean, K Cho, R Memisevic, Y Bengio arXiv preprint arXiv:1412.2007, 2014 | 1255 | 2014 |
| Adversarial learning for neural dialogue generation J Li, W Monroe, T Shi, S Jean, A Ritter, D Jurafsky arXiv preprint arXiv:1701.06547, 2017 | 1143 | 2017 |
| Theano: A Python framework for fast computation of mathematical expressions R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... arXiv e-prints, arXiv: 1605.02688, 2016 | 1137* | 2016 |
| Emonets: Multimodal deep learning approaches for emotion recognition in video SE Kahou, X Bouthillier, P Lamblin, C Gulcehre, V Michalski, K Konda, ... Journal on Multimodal User Interfaces 10, 99-111, 2016 | 522 | 2016 |
| Combining modality specific deep neural networks for emotion recognition in video SE Kahou, C Pal, X Bouthillier, P Froumenty, Ç Gülçehre, R Memisevic, ... Proceedings of the 15th ACM on International conference on multimodal …, 2013 | 444 | 2013 |
| Lingvo: a modular and scalable framework for sequence-to-sequence modeling J Shen, P Nguyen, Y Wu, Z Chen, MX Chen, Y Jia, A Kannan, T Sainath, ... arXiv preprint arXiv:1902.08295, 2019 | 214 | 2019 |
| Montreal neural machine translation systems for WMT’15 S Jean, O Firat, K Cho, R Memisevic, Y Bengio Proceedings of the tenth workshop on statistical machine translation, 134-140, 2015 | 187 | 2015 |
| Does neural machine translation benefit from larger context? S Jean, S Lauly, O Firat, K Cho arXiv preprint arXiv:1704.05135, 2017 | 165 | 2017 |
| Embedding word similarity with neural machine translation F Hill, K Cho, S Jean, C Devin, Y Bengio arXiv preprint arXiv:1412.6448, 2014 | 69 | 2014 |
| Not all neural embeddings are born equal F Hill, KH Cho, S Jean, C Devin, Y Bengio arXiv preprint arXiv:1410.0718, 2014 | 55 | 2014 |
| Theano: A Python framework for fast computation of mathematical expressions. arXiv R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... arXiv preprint arXiv:1605.02688 10, 2016 | 51 | 2016 |
| Adaptive Scheduling for Multi-Task Learning S Jean, O Firat, M Johnson Continual Learning Workshop at NeurIPS, 2018 | 42 | 2018 |
| The representational geometry of word meanings acquired by neural machine translation models F Hill, K Cho, S Jean, Y Bengio Machine Translation 31, 3-18, 2017 | 36 | 2017 |
| Context-aware learning for neural machine translation S Jean, K Cho arXiv preprint arXiv:1903.04715, 2019 | 22 | 2019 |
| Neural machine translation for cross-lingual pronoun prediction S Jean, S Lauly, O Firat, K Cho Proceedings of the third workshop on discourse in machine translation, 54-57, 2017 | 18 | 2017 |
| Fill in the blanks: Imputing missing sentences for larger-context neural machine translation S Jean, A Bapna, O Firat arXiv preprint arXiv:1910.14075, 2019 | 8 | 2019 |
| Log-linear reformulation of the noisy channel model for document-level neural machine translation S Jean, K Cho Proceedings of the Fourth Workshop on Structured Prediction for NLP, 95-101, 2020 | 7 | 2020 |
| Measuring and Mitigating Constraint Violations of In-Context Learning for Utterance-to-API Semantic Parsing S Wang, S Jean, S Sengupta, J Gung, N Pappas, Y Zhang arXiv preprint arXiv:2305.15338, 2023 | 4 | 2023 |
| EmoNets: Multimodal deep learning approaches for emotion recognition in video S Ebrahimi Kahou, X Bouthillier, P Lamblin, C Gulcehre, V Michalski, ... arXiv e-prints, arXiv: 1503.01800, 2015 | 2 | 2015 |
| Measuring and mitigating dialog-to-API constraint violations of in-context learning S Wang, S Jean, S Sengupta, J Gung, N Pappas, Y Zhang | | 2023 |
