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 flows transform a simple base distribution into a complex targetdistribution and have proved to be powerful models for data generation anddensity estimation. In this work, we propose a novel type of normalizing flowdriven by a differential deformation of the Wiener process. As a result, weobtain a rich time series model whose observable process inherits many of theappealing properties of its base process, such as efficient computation oflikelihoods and marginals. Furthermore, our continuous treatment provides anatural framework for irregular time series with an independent arrivalprocess, including straightforward interpolation. We illustrate the desirableproperties of the proposed model on popular stochastic processes anddemonstrate its superior flexibility to variational RNN and latent ODEbaselines in a series of experiments on synthetic and real-world data.

Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows