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

Research on time-series similarity measures has emphasized the need forelastic methods which align the indices of pairs of time series and a plethoraof non-parametric have been proposed for the task. On the other hand, deeplearning approaches are dominant in closely related domains, such as learningimage and text sentence similarity. In this paper, we propose\textit{NeuralWarp}, a novel measure that models the alignment of time-seriesindices in a deep representation space, by modeling a warping function as anupper level neural network between deeply-encoded time series values.Experimental results demonstrate that \textit{NeuralWarp} outperforms bothnon-parametric and un-warped deep models on a range of diverse real-lifedatasets.

NeuralWarp: Time-Series Similarity with Warping Networks