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 present an approach to deep estimation of discrete conditional probabilitydistributions. Such models have several applications, including generativemodeling of audio, image, and video data. Our approach combines two maintechniques: dyadic partitioning and graph-based smoothing of the discretespace. By recursively decomposing each dimension into a series of binary splitsand smoothing over the resulting distribution using graph-based trendfiltering, we impose a strict structure to the model and achieve much highersample efficiency. We demonstrate the advantages of our model through a seriesof benchmarks on both synthetic and real-world datasets, in some cases reducingthe error by nearly half in comparison to other popular methods in theliterature. All of our models are implemented in Tensorflow and publiclyavailable at https://github.com/tansey/sdp .

Deep Nonparametric Estimation of Discrete Conditional Distributions via Smoothed Dyadic Partitioning