English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper considers the generation of prediction intervals (PIs) by neuralnetworks for quantifying uncertainty in regression tasks. It is axiomatic thathigh-quality PIs should be as narrow as possible, whilst capturing a specifiedportion of data. We derive a loss function directly from this axiom thatrequires no distributional assumption. We show how its form derives from alikelihood principle, that it can be used with gradient descent, and that modeluncertainty is accounted for in ensembled form. Benchmark experiments show themethod outperforms current state-of-the-art uncertainty quantification methods,reducing average PI width by over 10%.

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