| Deep neural networks improve radiologists’ performance in breast cancer screening N Wu, J Phang, J Park, Y Shen, Z Huang, M Zorin, S Jastrzębski, T Févry, ... IEEE transactions on medical imaging 39 (4), 1184-1194, 2019 | 439 | 2019 |
| Sentence encoders on stilts: Supplementary training on intermediate labeled-data tasks J Phang, T Févry, SR Bowman arXiv preprint arXiv:1811.01088, 2018 | 353 | 2018 |
| Bloom: A 176b-parameter open-access multilingual language model TL Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, R Castagné, ... arXiv preprint arXiv:2211.05100, 2022 | 199 | 2022 |
| Intermediate-task transfer learning with pretrained models for natural language understanding: When and why does it work? Y Pruksachatkun, J Phang, H Liu, PM Htut, X Zhang, RY Pang, C Vania, ... arXiv preprint arXiv:2005.00628, 2020 | 197 | 2020 |
| Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022 | 177 | 2022 |
| The pile: An 800gb dataset of diverse text for language modeling L Gao, S Biderman, S Black, L Golding, T Hoppe, C Foster, J Phang, H He, ... arXiv preprint arXiv:2101.00027, 2020 | 157 | 2020 |
| Gpt-neox-20b: An open-source autoregressive language model S Black, S Biderman, E Hallahan, Q Anthony, L Gao, L Golding, H He, ... arXiv preprint arXiv:2204.06745, 2022 | 154 | 2022 |
| Do attention heads in BERT track syntactic dependencies? PM Htut, J Phang, S Bordia, SR Bowman arXiv preprint arXiv:1911.12246, 2019 | 101 | 2019 |
| Investigating BERT's knowledge of language: five analysis methods with NPIs A Warstadt, Y Cao, I Grosu, W Peng, H Blix, Y Nie, A Alsop, S Bordia, ... arXiv preprint arXiv:1909.02597, 2019 | 101 | 2019 |
| An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization Y Shen, N Wu, J Phang, J Park, K Liu, S Tyagi, L Heacock, SG Kim, L Moy, ... Medical image analysis 68, 101908, 2021 | 100 | 2021 |
| Unsupervised sentence compression using denoising auto-encoders T Fevry, J Phang arXiv preprint arXiv:1809.02669, 2018 | 67 | 2018 |
| English intermediate-task training improves zero-shot cross-lingual transfer too J Phang, I Calixto, PM Htut, Y Pruksachatkun, H Liu, C Vania, K Kann, ... arXiv preprint arXiv:2005.13013, 2020 | 59 | 2020 |
| jiant 1.2: A software toolkit for research on general-purpose text understanding models A Wang, IF Tenney, Y Pruksachatkun, K Yu, J Hula, P Xia, R Pappagari, ... Note: http://jiant. info/Cited by: footnote 4, 2019 | 47 | 2019 |
| jiant: A software toolkit for research on general-purpose text understanding models Y Pruksachatkun, P Yeres, H Liu, J Phang, PM Htut, A Wang, I Tenney, ... arXiv preprint arXiv:2003.02249, 2020 | 32 | 2020 |
| Globally-aware multiple instance classifier for breast cancer screening Y Shen, N Wu, J Phang, J Park, G Kim, L Moy, K Cho, KJ Geras Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019 …, 2019 | 29 | 2019 |
| BBQ: A hand-built bias benchmark for question answering A Parrish, A Chen, N Nangia, V Padmakumar, J Phang, J Thompson, ... arXiv preprint arXiv:2110.08193, 2021 | 28 | 2021 |
| What Language Model to Train if You Have One Million GPU Hours? TL Scao, T Wang, D Hesslow, L Saulnier, S Bekman, MS Bari, S Bideman, ... arXiv preprint arXiv:2210.15424, 2022 | 25 | 2022 |
| The NYU breast cancer screening dataset V1. 0 N Wu, J Phang, J Park, Y Shen, SG Kim, L Heacock, L Moy, K Cho, ... New York Univ., New York, NY, USA, Tech. Rep, 2019 | 24 | 2019 |
| QuALITY: Question Answering with Long Input Texts, Yes! RY Pang, A Parrish, N Joshi, N Nangia, J Phang, A Chen, V Padmakumar, ... arXiv preprint arXiv:2112.08608, 2021 | 22 | 2021 |
| Comparing test sets with item response theory C Vania, PM Htut, W Huang, D Mungra, RY Pang, J Phang, H Liu, K Cho, ... arXiv preprint arXiv:2106.00840, 2021 | 18 | 2021 |
Samuel R. BowmanAssociate Professor, NYUVerified email at nyu.edu
