Quantifying Deep Learning Model Uncertainty in Conformal Prediction

no code implementations • 1 Jun 2023 • Hamed Karimi, Reza Samavi

Precise estimation of predictive uncertainty in deep neural networks is a critical requirement for reliable decision-making in machine learning and statistical modeling, particularly in the context of medical AI.

Conformal Prediction Decision Making +1

Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition

no code implementations • 16 Aug 2016 • Hamed Karimi, Julie Nutini, Mark Schmidt

In 1963, Polyak proposed a simple condition that is sufficient to show a global linear convergence rate for gradient descent.