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

Generative-adversarial networks (GANs) have been used to produce data closelyresembling example data in a compressed, latent space that is close tosufficient for reconstruction in the original vector space. The Wassersteinmetric has been used as an alternative to binary cross-entropy, producing morenumerically stable GANs with greater mode covering behavior. Here, ageneralization of the Wasserstein distance, using higher-order moments than themean, is derived. Training a GAN with this higher-order Wasserstein metric isdemonstrated to exhibit superior performance, even when adjusted for slightlyhigher computational cost. This is illustrated generating synthetic antibodysequences.

Adversarial network training using higher-order moments in a modified Wasserstein distance