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

In variational autoencoders, the prior on the latent codes $z$ is oftentreated as an afterthought, but the prior shapes the kind of latentrepresentation that the model learns. If the goal is to learn a representationthat is interpretable and useful, then the prior should reflect the ways inwhich the high-level factors that describe the data vary. The "default" prioris an isotropic normal, but if the natural factors of variation in the datasetexhibit discrete structure or are not independent, then the isotropic-normalprior will actually encourage learning representations that mask thisstructure. To alleviate this problem, we propose using a flexible Bayesiannonparametric hierarchical clustering prior based on the time-marginalizedcoalescent (TMC). To scale learning to large datasets, we develop a newinducing-point approximation and inference algorithm. We then apply the methodwithout supervision to several datasets and examine the interpretability andpractical performance of the inferred hierarchies and learned latent space.

The LORACs prior for VAEs: Letting the Trees Speak for the Data