R. Rane

Acoustic noise, even when not diagnostically informative, can systematically manipulate Alzheimer's predictions in SSL models, revealing a critical vulnerability in current speech-based assessments.

Omitted variable bias in deep learning can be effectively controlled, leading to unbiased predictions and improved model performance even in the presence of confounding variables.

ICON decomposition reveals the true reliance of deep models on concepts, debunking misleading correlations that traditional methods often overlook.