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

Learning generative models and inferring latent trajectories have shown to bechallenging for time series due to the intractable marginal likelihoods offlexible generative models. It can be addressed by surrogate objectives foroptimization. We propose Monte Carlo filtering objectives (MCFOs), a family ofvariational objectives for jointly learning parametric generative models andamortized adaptive importance proposals of time series. MCFOs extend thechoices of likelihood estimators beyond Sequential Monte Carlo instate-of-the-art objectives, possess important properties revealing the factorsfor the tightness of objectives, and allow for less biased and variant gradientestimates. We demonstrate that the proposed MCFOs and gradient estimations leadto efficient and stable model learning, and learned generative models wellexplain data and importance proposals are more sample efficient on variouskinds of time series data.

Monte Carlo Filtering Objectives: A New Family of Variational Objectives to Learn Generative Model and Neural Adaptive Proposal for Time Series