Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Distribution-Free Data Uncertainty for Neural Network Regression
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Proposes a distribution-free approach to estimating data (aleatoric) uncertainty in neural network regression, as an alternative to parametric output distributions.
Problem
Regression models usually predict only the expected target value, lacking data uncertainty estimates; existing probabilistic extensions assume parametric output distributions.
Approach
Instead of assuming a parametric distribution around the predicted mean, the method estimates data uncertainty in a distribution-free manner for neural network regressors, analogous to how classifiers output class probabilities.
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