Tuning Monte Carlo Dropout hyperparameters with GWO, BO, or PSO and adding a predictive-entropy loss term reportedly improves accuracy, uncertainty accuracy, and calibration by 2-3% over vanilla MCD.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification
Tuning Monte Carlo Dropout hyperparameters with GWO, BO, or PSO and adding a predictive-entropy loss term reportedly improves accuracy, uncertainty accuracy, and calibration by 2-3% over vanilla MCD.