22 citations · 39 across the 2 of their papers we have counts for
4 papers
Empirical Evaluation of Pretraining Strategies for Supervised Entity Linking
Thibault Févry, Nicholas FitzGerald, Livio Baldini Soares +1
In this work, we present an entity linking model which combines a Transformer architecture with large scale pretraining from Wikipedia links. Our model achieves the state-of-the-ar…
Learning Cross-Context Entity Representations from Text
Jeffrey Ling, Nicholas FitzGerald, Zifei Shan +4
Language modeling tasks, in which words, or word-pieces, are predicted on the basis of a local context, have been very effective for learning word embeddings and context dependent…
Improving localization-based approaches for breast cancer screening exam classification
Thibault Févry, Jason Phang, Nan Wu +4
We trained and evaluated a localization-based deep CNN for breast cancer screening exam classification on over 200,000 exams (over 1,000,000 images). Our model achieves an AUC of 0…
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
Nan Wu, Jason Phang, Jungkyu Park +29
We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200,000 exams (over 1,000,000 images). Our network ach…