10 citations · 13 across the 4 of their papers we have counts for
4 papers
Boosting the interpretability of clinical risk scores with intervention predictions
Eric Loreaux, Ke Yu, Jonas Kemp +8
Machine learning systems show significant promise for forecasting patient adverse events via risk scores. However, these risk scores implicitly encode assumptions about future inte…
Can contrastive learning avoid shortcut solutions?
Joshua Robinson, Li Sun, Ke Yu +3
The generalization of representations learned via contrastive learning depends crucially on what features of the data are extracted. However, we observe that the contrastive loss d…
Context Matters: Graph-based Self-supervised Representation Learning for Medical Images
Li Sun, Ke Yu, Kayhan Batmanghelich
Supervised learning method requires a large volume of annotated datasets. Collecting such datasets is time-consuming and expensive. Until now, very few annotated COVID-19 imaging d…
Semi-Supervised Hierarchical Drug Embedding in Hyperbolic Space
Ke Yu, Shyam Visweswaran, Kayhan Batmanghelich
Learning accurate drug representation is essential for tasks such as computational drug repositioning and prediction of drug side-effects. A drug hierarchy is a valuable source tha…