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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We propose a novel interpretation technique to explain the behavior ofstructured output models, which learn mappings between an input vector to a setof output variables simultaneously. Because of the complex relationship betweenthe computational path of output variables in structured models, a feature canaffect the value of output through other ones. We focus on one of the outputsas the target and try to find the most important features utilized by thestructured model to decide on the target in each locality of the input space.In this paper, we assume an arbitrary structured output model is available as ablack box and argue how considering the correlations between output variablescan improve the explanation performance. The goal is to train a function as aninterpreter for the target output variable over the input space. We introducean energy-based training process for the interpreter function, whicheffectively considers the structural information incorporated into the model tobe explained. The effectiveness of the proposed method is confirmed using avariety of simulated and real data sets.

SOInter: A Novel Deep Energy Based Interpretation Method for Explaining Structured Output Models