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

Normalizing flow (NF) has gained popularity over traditional maximumlikelihood based methods due to its strong capability to model complex datadistributions. However, the standard approach, which maps the observed data toa normal distribution, has difficulty in handling data distributions withmultiple relatively isolated modes. To overcome this issue, we propose a newframework based on variational latent representation to improve the practicalperformance of NF. The idea is to replace the standard normal latent variablewith a more general latent representation, jointly learned via VariationalBayes. For example, by taking the latent representation as a discrete sequence,our framework can learn a Transformer model that generates the latent sequenceand an NF model that generates continuous data distribution conditioned on thesequence. The resulting method is significantly more powerful than the standardnormalization flow approach for generating data distributions with multiplemodes. Extensive experiments have shown the advantages of NF with variationallatent representation.

Normalizing Flow with Variational Latent Representation