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

Methods addressing spurious correlations such as Just Train Twice (JTT,arXiv:2107.09044v2) involve reweighting a subset of the training set tomaximize the worst-group accuracy. However, the reweighted set of examples maypotentially contain unlearnable examples that hamper the model's learning. Wepropose mitigating this by detecting outliers to the training set and removingthem before reweighting. Our experiments show that our method achievescompetitive or better accuracy compared with JTT and can detect and removeannotation errors in the subset being reweighted in JTT.

Outlier-Aware Training for Improving Group Accuracy Disparities