Neural activation coverage can be adapted to provide uncertainty estimates in regression that the authors' experiments show are more meaningful than Monte-Carlo Dropout.
Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks
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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.