Multi-level Monte Carlo Dropout for Efficient Uncertainty Quantification

Aaron Pim, Tristan Pryer

We develop a multilevel Monte Carlo (MLMC) framework for uncertainty quantification with Monte Carlo dropout. Treating dropout masks as a source of epistemic randomness, we define…