| Transformers: State-of-the-Art Natural Language Processing T Wolf arXiv preprint arXiv:1910.03771, 2020 | 9385 | 2020 |
| A neural attention model for abstractive sentence summarization AM Rush arXiv preprint arXiv:1509.00685, 2015 | 3621 | 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 | 2359 | 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 | 2240 | 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 | 1767 | 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, ... | 1747 | 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 | 1329 | 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 | 1222 | 2016 |
| Sequence-level knowledge distillation Y Kim, AM Rush arXiv preprint arXiv:1606.07947, 2016 | 1185 | 2016 |
| Bottom-up abstractive summarization S Gehrmann, Y Deng, AM Rush arXiv preprint arXiv:1808.10792, 2018 | 864 | 2018 |
| Sequence-to-sequence learning as beam-search optimization S Wiseman, AM Rush arXiv preprint arXiv:1606.02960, 2016 | 683 | 2016 |
| Challenges in data-to-document generation S Wiseman, SM Shieber, AM Rush arXiv preprint arXiv:1707.08052, 2017 | 682 | 2017 |
| Structured attention networks Y Kim, C Denton, L Hoang, AM Rush arXiv preprint arXiv:1702.00887, 2017 | 651 | 2017 |
| Gltr: Statistical detection and visualization of generated text S Gehrmann, H Strobelt, AM Rush arXiv preprint arXiv:1906.04043, 2019 | 579 | 2019 |
| 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 | 568 | 2017 |
| Movement pruning: Adaptive sparsity by fine-tuning V Sanh, T Wolf, A Rush Advances in neural information processing systems 33, 20378-20389, 2020 | 491 | 2020 |
| Zephyr: Direct distillation of lm alignment L Tunstall, E Beeching, N Lambert, N Rajani, K Rasul, Y Belkada, ... arXiv preprint arXiv:2310.16944, 2023 | 480 | 2023 |
| Parameter-efficient transfer learning with diff pruning D Guo, AM Rush, Y Kim arXiv preprint arXiv:2012.07463, 2020 | 412 | 2020 |
| Adversarially regularized autoencoders J Zhao, Y Kim, K Zhang, A Rush, Y LeCun International conference on machine learning, 5902-5911, 2018 | 372 | 2018 |
| 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 | 361* | 2017 |
Yoon KimAssistant Professor, MIT確認したメール アドレス: mit.edu