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Jason Phang
Jason Phang
Verified email at nyu.edu - Homepage
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Year
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
2912019
Sentence encoders on stilts: Supplementary training on intermediate labeled-data tasks
J Phang, T Févry, SR Bowman
arXiv preprint arXiv:1811.01088, 2018
2742018
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
1332020
Do attention heads in BERT track syntactic dependencies?
PM Htut, J Phang, S Bordia, SR Bowman
arXiv preprint arXiv:1911.12246, 2019
802019
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
722019
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
632021
Unsupervised sentence compression using denoising auto-encoders
T Fevry, J Phang
arXiv preprint arXiv:1809.02669, 2018
572018
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
492020
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
492020
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
382019
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
272020
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
International workshop on machine learning in medical imaging, 18-26, 2019
202019
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
152019
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
142022
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
122021
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
62021
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
62021
Improving localization-based approaches for breast cancer screening exam classification
T Févry, J Phang, N Wu, S Kim, L Moy, K Cho, KJ Geras
arXiv preprint arXiv:1908.00615, 2019
62019
Adversarially constructed evaluation sets are more challenging, but may not be fair
J Phang, A Chen, W Huang, SR Bowman
arXiv preprint arXiv:2111.08181, 2021
52021
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
32022
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