| Transformers: State-of-the-art natural language processing T Wolf, L Debut, V Sanh, J Chaumond, C Delangue, A Moi, P Cistac, ... Proceedings of the 2020 conference on empirical methods in natural language …, 2020 | 8030* | 2020 |
| A neural attention model for abstractive sentence summarization AM Rush, S Chopra, J Weston arXiv preprint arXiv:1509.00685, 2015 | 3520 | 2015 |
| Opennmt: Open-source toolkit for neural machine translation G Klein, Y Kim, Y Deng, J Senellart, AM Rush arXiv preprint arXiv:1701.02810, 2017 | 2278 | 2017 |
| Character-aware neural language models Y Kim, Y Jernite, D Sontag, A Rush Proceedings of the AAAI conference on artificial intelligence 30 (1), 2016 | 2172 | 2016 |
| Multitask prompted training enables zero-shot task generalization V Sanh, A Webson, C Raffel, SH Bach, L Sutawika, Z Alyafeai, A Chaffin, ... arXiv preprint arXiv:2110.08207, 2021 | 1481 | 2021 |
| Bloom: A 176b-parameter open-access multilingual language model T Le Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, R Castagné, ... | 1434 | 2023 |
| Towards ai-complete question answering: A set of prerequisite toy tasks J Weston, A Bordes, S Chopra, AM Rush, B Van Merriënboer, A Joulin, ... arXiv preprint arXiv:1502.05698, 2015 | 1283 | 2015 |
| 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 | 1190 | 2016 |
| Sequence-level knowledge distillation Y Kim, AM Rush arXiv preprint arXiv:1606.07947, 2016 | 1085 | 2016 |
| Bottom-up abstractive summarization S Gehrmann, Y Deng, AM Rush arXiv preprint arXiv:1808.10792, 2018 | 827 | 2018 |
| Sequence-to-sequence learning as beam-search optimization S Wiseman, AM Rush arXiv preprint arXiv:1606.02960, 2016 | 658 | 2016 |
| Challenges in data-to-document generation S Wiseman, SM Shieber, AM Rush arXiv preprint arXiv:1707.08052, 2017 | 653 | 2017 |
| Structured attention networks Y Kim, C Denton, L Hoang, AM Rush arXiv preprint arXiv:1702.00887, 2017 | 611 | 2017 |
| Lstmvis: A tool for visual analysis of hidden state dynamics in recurrent neural networks H Strobelt, S Gehrmann, H Pfister, AM Rush IEEE transactions on visualization and computer graphics 24 (1), 667-676, 2017 | 531 | 2017 |
| Gltr: Statistical detection and visualization of generated text S Gehrmann, H Strobelt, AM Rush arXiv preprint arXiv:1906.04043, 2019 | 464 | 2019 |
| Movement pruning: Adaptive sparsity by fine-tuning V Sanh, T Wolf, A Rush Advances in neural information processing systems 33, 20378-20389, 2020 | 421 | 2020 |
| Adversarially regularized autoencoders J Zhao, Y Kim, K Zhang, A Rush, Y LeCun International conference on machine learning, 5902-5911, 2018 | 357 | 2018 |
| Parameter-efficient transfer learning with diff pruning D Guo, AM Rush, Y Kim arXiv preprint arXiv:2012.07463, 2020 | 331 | 2020 |
| Image-to-markup generation with coarse-to-fine attention Y Deng, A Kanervisto, J Ling, AM Rush International Conference on Machine Learning, 980-989, 2017 | 331* | 2017 |
| Commonsense knowledge mining from pretrained models J Davison, J Feldman, AM Rush Proceedings of the 2019 conference on empirical methods in natural language …, 2019 | 322 | 2019 |
Yoon KimAssistant Professor, MIT在 mit.edu 的电子邮件经过验证
