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

Deep neural networks are in the limelight of machine learning with theirexcellent performance in many data-driven applications. However, they can leadto inaccurate predictions when queried in out-of-distribution data points,which can have detrimental effects especially in sensitive domains, such ashealthcare and transportation, where erroneous predictions can be very costlyand/or dangerous. Subsequently, quantifying the uncertainty of the output of aneural network is often leveraged to evaluate the confidence of itspredictions, and ensemble models have proved to be effective in measuring theuncertainty by utilizing the variance of predictions over a pool of models. Inthis paper, we propose a novel approach for uncertainty quantification viaensembles, called Random Activation Functions (RAFs) Ensemble, that aims atimproving the ensemble diversity toward a more robust estimation, byaccommodating each neural network with a different (random) activationfunction. Extensive empirical study demonstrates that RAFs Ensemble outperformsstate-of-the-art ensemble uncertainty quantification methods on both syntheticand real-world datasets in a series of regression tasks.

Toward Robust Uncertainty Estimation with Random Activation Functions