1 code implementation • 15 May 2019 • Tim Pearce, Russell Tsuchida, Mohamed Zaki, Alexandra Brintrup, Andy Neely
A simple, flexible approach to creating expressive priors in Gaussian process (GP) models makes new kernels from a combination of basic kernels, e. g. summing a periodic and linear kernel can capture seasonal variation with a long term trend.
no code implementations • 27 Nov 2018 • Tim Pearce, Mohamed Zaki, Andy Neely
Ensembles of neural networks (NNs) have long been used to estimate predictive uncertainty; a small number of NNs are trained from different initialisations and sometimes on differing versions of the dataset.
2 code implementations • 12 Oct 2018 • Tim Pearce, Felix Leibfried, Alexandra Brintrup, Mohamed Zaki, Andy Neely
Ensembling NNs provides an easily implementable, scalable method for uncertainty quantification, however, it has been criticised for not being Bayesian.
2 code implementations • 29 May 2018 • Tim Pearce, Nicolas Anastassacos, Mohamed Zaki, Andy Neely
The use of ensembles of neural networks (NNs) for the quantification of predictive uncertainty is widespread.
1 code implementation • ICML 2018 • Tim Pearce, Mohamed Zaki, Alexandra Brintrup, Andy Neely
This paper considers the generation of prediction intervals (PIs) by neural networks for quantifying uncertainty in regression tasks.