732 citations · 802 across the 11 of their papers we have counts for
3 papers · 1 filter
Adversarial Contrastive Pre-training for Protein Sequences
Matthew B. A. McDermott, Brendan Yap, Harry Hsu +2
Recent developments in Natural Language Processing (NLP) demonstrate that large-scale, self-supervised pre-training can be extremely beneficial for downstream tasks. These ideas ha…
Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings
Haoran Zhang, Amy X. Lu, Mohamed Abdalla +2
In this work, we examine the extent to which embeddings may encode marginalized populations differently, and how this may lead to a perpetuation of biases and worsened performance…
Publicly Available Clinical BERT Embeddings
Emily Alsentzer, John R. Murphy, Willie Boag +4
Contextual word embedding models such as ELMo (Peters et al., 2018) and BERT (Devlin et al., 2018) have dramatically improved performance for many natural language processing (NLP)…