Monte Carlo Stochastic Depth provides a theoretically linked and empirically competitive method for uncertainty quantification in modern deep learning models such as object detectors.
Probabilistic consis- tency in machine learning and its connection to uncertainty quantification
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Monte Carlo Stochastic Depth for Uncertainty Estimation in Deep Learning
Monte Carlo Stochastic Depth provides a theoretically linked and empirically competitive method for uncertainty quantification in modern deep learning models such as object detectors.