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

Machine learning models encounter Out-of-Distribution (OoD) errors when thedata seen at test time are generated from a different stochastic generator thanthe one used to generate the training data. One proposal to scale OoD detectionto high-dimensional data is to learn a tractable likelihood approximation ofthe training distribution, and use it to reject unlikely inputs. However,likelihood models on natural data are themselves susceptible to OoD errors, andeven assign large likelihoods to samples from other datasets. To mitigate thisproblem, we propose Generative Ensembles, which robustify density-based OoDdetection by way of estimating epistemic uncertainty of the likelihood model.We present a puzzling observation in need of an explanation -- althoughlikelihood measures cannot account for the typical set of a distribution, andtherefore should not be suitable on their own for OoD detection, WAIC performssurprisingly well in practice.

WAIC, but Why? Generative Ensembles for Robust Anomaly Detection