Advances in Bayesian Model Selection and Uncertainty Estimation for Deep Learning

Deep learning has achieved remarkable success across various fields, such as computer vision, natural language processing, and scientific problems, enabled by the ability of deep neural networks to learn complex patterns from large amounts of data. Yet, despite these advances, several key limitations that can hinder their application to real-world problems remain.