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

Concept bottleneck models perform classification by first predicting which ofa list of human provided concepts are true about a datapoint. Then a downstreammodel uses these predicted concept labels to predict the target label. Thepredicted concepts act as a rationale for the target prediction. Model trustissues emerge in this paradigm when soft concept labels are used: it haspreviously been observed that extra information about the data distributionleaks into the concept predictions. In this work we show how Monte-CarloDropout can be used to attain soft concept predictions that do not containleaked information.

Towards learning to explain with concept bottleneck models: mitigating information leakage