565 citations · 1.8k across the 38 of their papers we have counts for
7 papers · 1 filter
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
Leo Gao, Stella Biderman, Sid Black +9
Recent work has demonstrated that increased training dataset diversity improves general cross-domain knowledge and downstream generalization capability for large-scale language mod…
Investigating and Simplifying Masking-based Saliency Methods for Model Interpretability
Jason Phang, Jungkyu Park, Krzysztof J. Geras
Saliency maps that identify the most informative regions of an image for a classifier are valuable for model interpretability. A common approach to creating saliency maps involves…
Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms
Nan Wu, Zhe Huang, Yiqiu Shen +8
Breast cancer is the most common cancer in women, and hundreds of thousands of unnecessary biopsies are done around the world at a tremendous cost. It is crucial to reduce the rate…
Intermediate-Task Transfer Learning with Pretrained Models for Natural Language Understanding: When and Why Does It Work?
Yada Pruksachatkun, Jason Phang, Haokun Liu +6
While pretrained models such as BERT have shown large gains across natural language understanding tasks, their performance can be improved by further training the model on a data-r…
English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too
Jason Phang, Iacer Calixto, Phu Mon Htut +5
Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on…
jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models
Yada Pruksachatkun, Phil Yeres, Haokun Liu +5
We introduce jiant, an open source toolkit for conducting multitask and transfer learning experiments on English NLU tasks. jiant enables modular and configuration-driven experimen…
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
Yiqiu Shen, Nan Wu, Jason Phang +8
Medical images differ from natural images in significantly higher resolutions and smaller regions of interest. Because of these differences, neural network architectures that work…