82 citations · 82 across the 2 of their papers we have counts for
3 papers
A Theoretical Framework for Understanding and Building "Any-task inference" Capable Foundation Models
Matthew McDermott, Florent Pollet, Payal Chandak +4
In this work, we provide a formal framework and mathematical definition for the subset of foundation models focused on enabling the property of "any-task inference" (ATI) (similar…
Learning Normal Representations for Blood Biomarkers
Aashna P. Shah, Michelle M. Li, Yash Lal +9
Blood-based biomarkers underpin clinical diagnosis and management, yet their interpretation relies largely on fixed population reference intervals that ignore stable, intra-patient…
Explaining Anomalies Detected by Autoencoders Using SHAP
Liat Antwarg, Ronnie Mindlin Miller, Bracha Shapira +1
Anomaly detection algorithms are often thought to be limited because they don't facilitate the process of validating results performed by domain experts. In Contrast, deep learning…