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

We propose a method for jointly inferring labels across a collection of datasamples, where each sample consists of an observation and a prior belief aboutthe label. By implicitly assuming the existence of a generative model for whicha differentiable predictor is the posterior, we derive a training objectivethat allows learning under weak beliefs. This formulation unifies variousmachine learning settings; the weak beliefs can come in the form of noisy orincomplete labels, likelihoods given by a different prediction mechanism onauxiliary input, or common-sense priors reflecting knowledge about thestructure of the problem at hand. We demonstrate the proposed algorithms ondiverse problems: classification with negative training examples, learning fromrankings, weakly and self-supervised aerial imagery segmentation,co-segmentation of video frames, and coarsely supervised text classification.

Resolving label uncertainty with implicit posterior models