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

Upcoming surveys such as the Vera C. Rubin Observatory Legacy Survey of Spaceand Time (LSST) will detect up to 10 million time-varying sources in the skyevery night for ten years. This information will be transmitted in a continuousstream to brokers that will select the most promising events for a variety ofscience cases using machine learning algorithms. We study the benefits andchallenges of Bayesian Neural Networks (BNNs) for this type of classificationtasks. BNNs are found to be accurate classifiers which also provide additionalinformation: they quantify the classification uncertainty which can beharnessed to analyse this upcoming data avalanche more efficiently.

Bayesian Neural Networks for classification tasks in the Rubin big data era