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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Building reliable AI decision support systems requires a robust set of dataon which to train models; both with respect to quantity and diversity.Obtaining such datasets can be difficult in resource limited settings, or forapplications in early stages of deployment. Sample rejection is one way to workaround this challenge, however much of the existing work in this area isill-suited for such scenarios. This paper substantiates that position andproposes a simple solution as a proof of concept baseline.

A Case for Rejection in Low Resource ML Deployment