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

Recent advances in Deep Gaussian Processes (DGPs) show the potential to havemore expressive representation than that of traditional Gaussian Processes(GPs). However, there exists a pathology of deep Gaussian processes that theirlearning capacities reduce significantly when the number of layers increases.In this paper, we present a new analysis in DGPs by studying its correspondingnonlinear dynamic systems to explain the issue. Existing work reports thepathology for the squared exponential kernel function. We extend ourinvestigation to four types of common stationary kernel functions. Therecurrence relations between layers are analytically derived, providing atighter bound and the rate of convergence of the dynamic systems. Wedemonstrate our finding with a number of experimental results.

Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems