Scaling vision models by depth and parameter count does not consistently improve localisation-based explanation quality across architectures, datasets, and post-hoc methods; smaller models often perform comparably or better.
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Shapley regression replaces the linear predictor in logistic regression with a k-additive cooperative game to detect APDS and other rare diseases from symptom data while remaining transparent.
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Scaling Vision Models Does Not Consistently Improve Localisation-Based Explanation Quality
Scaling vision models by depth and parameter count does not consistently improve localisation-based explanation quality across architectures, datasets, and post-hoc methods; smaller models often perform comparably or better.
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Shapley Regression for Rare Disease Diagnosis Support: a case study on APDS
Shapley regression replaces the linear predictor in logistic regression with a k-additive cooperative game to detect APDS and other rare diseases from symptom data while remaining transparent.