65 citations · 199 across the 12 of their papers we have counts for
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stat.ML2018
Uncertainty in Neural Networks: Approximately Bayesian Ensembling
Tim Pearce, Felix Leibfried, Alexandra Brintrup +2
Understanding the uncertainty of a neural network's (NN) predictions is essential for many purposes. The Bayesian framework provides a principled approach to this, however applying…
stat.ML2018
High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach
Tim Pearce, Mohamed Zaki, Alexandra Brintrup +1
This paper considers the generation of prediction intervals (PIs) by neural networks for quantifying uncertainty in regression tasks. It is axiomatic that high-quality PIs should b…