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

We develop an approach to training generative models based on unrolling avariational auto-encoder into a Markov chain, and shaping the chain'strajectories using a technique inspired by recent work in Approximate Bayesiancomputation. We show that the global minimizer of the resulting objective isachieved when the generative model reproduces the target distribution. To allowfiner control over the behavior of the models, we add a regularization terminspired by techniques used for regularizing certain types of policy search inreinforcement learning. We present empirical results on the MNIST and TFDdatasets which show that our approach offers state-of-the-art performance, bothquantitatively and from a qualitative point of view.

Variational Generative Stochastic Networks with Collaborative Shaping