Neural activation coverage can be adapted to provide uncertainty estimates in regression that the authors' experiments show are more meaningful than Monte-Carlo Dropout.
An introduction to variational inference
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Monte Carlo Stochastic Depth provides a theoretically linked and empirically competitive method for uncertainty quantification in modern deep learning models such as object detectors.
citing papers explorer
-
Revisiting Neural Activation Coverage for Uncertainty Estimation
Neural activation coverage can be adapted to provide uncertainty estimates in regression that the authors' experiments show are more meaningful than Monte-Carlo Dropout.
-
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.