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Mohamed Zaki

2 accepted papers

2019

Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions

UAI 2019poster

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. Despite a well-studied link between GPs and Bayesian neura…

Cited by 67SourcePDFScholar
2018

High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach

ICML 2018oral

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 be as narrow as possible, whilst capturing a specified portion of data. We derive a loss function directly from this axio…

Cited by 374SourcePDFScholar