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Found 4 papers, 3 papers with code
Likelihood-ratio-based confidence intervals for neural networks
This paper introduces a first implementation of a novel likelihood-ratio-based approach for constructing confidence intervals for neural networks.
Optimal Training of Mean Variance Estimation Neural Networks
We demonstrate, both on toy examples and on a number of benchmark UCI regression data sets, that following the original recommendations and the novel separate regularization can lead to significant improvements.
Confident Neural Network Regression with Bootstrapped Deep Ensembles
A classical parametric model has uncertainty in the parameters due to the fact that the data on which the model is build is a random sample.
How to Evaluate Uncertainty Estimates in Machine Learning for Regression?
We show why it is fundamentally flawed to test a prediction or confidence interval on a single test set.
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