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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We study the long time behavior of an underdamped mean-field Langevin (MFL)equation, and provide a general convergence as well as an exponentialconvergence rate result under different conditions. The results on the MFLequation can be applied to study the convergence of the Hamiltonian gradientdescent algorithm for the overparametrized optimization. We then provide anumerical example of the algorithm to train a generative adversarial networks(GAN).

Ergodicity of the underdamped mean-field Langevin dynamics