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

Conditional probabilistic graphical models provide a powerful framework forstructured regression in spatio-temporal datasets with complex correlationpatterns. However, in real-life applications a large fraction of observationsis often missing, which can severely limit the representational power of thesemodels. In this paper we propose a Marginalized Gaussian Conditional RandomFields (m-GCRF) structured regression model for dealing with missing labels inpartially observed temporal attributed graphs. This method is aimed at learningwith both labeled and unlabeled parts and effectively predicting future valuesin a graph. The method is even capable of learning from nodes for which theresponse variable is never observed in history, which poses problems for manystate-of-the-art models that can handle missing data. The proposed model ischaracterized for various missingness mechanisms on 500 synthetic graphs. Thebenefits of the new method are also demonstrated on a challenging applicationfor predicting precipitation based on partial observations of climate variablesin a temporal graph that spans the entire continental US. We also show that themethod can be useful for optimizing the costs of data collection in climateapplications via active reduction of the number of weather stations toconsider. In experiments on these real-world and synthetic datasets we showthat the proposed model is consistently more accurate than alternativesemi-supervised structured models, as well as models that either use imputationto deal with missing values or simply ignore them altogether.

Semi-supervised learning for structured regression on partially observed attributed graphs