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/

Zendy Plus

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

Zendy Tools

Zendy Open

When using machine learning (ML) techniques, users typically need to choose aplethora of algorithm-specific parameters, referred to as hyperparameters. Inthis paper, we compare the performance of two algorithms, particle swarmoptimisation (PSO) and Bayesian optimisation (BO), for the autonomousdetermination of these hyperparameters in applications to different ML taskstypical for the field of high energy physics (HEP). Our evaluation of theperformance includes a comparison of the capability of the PSO and BOalgorithms to make efficient use of the highly parallel computing resourcesthat are characteristic of contemporary HEP experiments.

Comparison of Bayesian and particle swarm algorithms for hyperparameter optimisation in machine learning applications in high energy physics