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

Hyper-parameter optimization is a crucial problem in machine learning as itaims to achieve the state-of-the-art performance in any model. Great effortshave been made in this field, such as random search, grid search, Bayesianoptimization. In this paper, we model hyper-parameter optimization process as aMarkov decision process, and tackle it with reinforcement learning. A novelhyper-parameter optimization method based on soft actor critic and hierarchicalmixture regularization has been proposed. Experiments show that the proposedmethod can obtain better hyper-parameters in a shorter time.

Hyper-parameter optimization based on soft actor critic and hierarchical mixture regularization