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

To improve the ability of VAE to disentangle in the latent space, existingworks mostly focus on enforcing independence among the learned latent factors.However, the ability of these models to disentangle often decreases as thecomplexity of the generative factors increases. In this paper, we investigatethe little-explored effect of the modeling capacity of a posterior density onthe disentangling ability of the VAE. We note that the independence within andthe complexity of the latent density are two different properties we constrainwhen regularizing the posterior density: while the former promotes thedisentangling ability of VAE, the latter -- if overly limited -- creates anunnecessary competition with the data reconstruction objective in VAE.Therefore, if we preserve the independence but allow richer modeling capacityin the posterior density, we will lift this competition and thereby allowimproved independence and data reconstruction at the same time. We investigatethis theoretical intuition with a VAE that utilizes a non-parametric latentfactor model, the Indian Buffet Process (IBP), as a latent density that is ableto grow with the complexity of the data. Across three widely-used benchmarkdata sets and two clinical data sets little explored for disentangled learning,we qualitatively and quantitatively demonstrated the improved disentanglingperformance of IBP-VAE over the state of the art. In the latter two clinicaldata sets riddled with complex factors of variations, we further demonstratedthat unsupervised disentangling of nuisance factors via IBP-VAE -- whencombined with a supervised objective -- can not only improve task accuracy incomparison to relevant supervised deep architectures but also facilitateknowledge discovery related to task decision-making. A shorter version of thiswork will appear in the ICDM 2019 conference proceedings.

Improving Disentangled Representation Learning with the Beta Bernoulli Process