Unreliable Uncertainty Estimates with Monte Carlo Dropout

Aslak Djupskås, Alexander Johannes Stasik, Signe Riemer-Sørensen

Reliable uncertainty estimation is crucial for machine learning models, especially in safety-critical domains. While exact Bayesian inference offers a principled approach, it is of…