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

Modern machine learning algorithms crucially rely on several design decisionsto achieve strong performance, making the problem of HyperparameterOptimization (HPO) more important than ever. Here, we combine the advantages ofthe popular bandit-based HPO method Hyperband (HB) and the evolutionary searchapproach of Differential Evolution (DE) to yield a new HPO method which we callDEHB. Comprehensive results on a very broad range of HPO problems, as well as awide range of tabular benchmarks from neural architecture search, demonstratethat DEHB achieves strong performance far more robustly than all previous HPOmethods we are aware of, especially for high-dimensional problems with discreteinput dimensions. For example, DEHB is up to 1000x faster than random search.It is also efficient in computational time, conceptually simple and easy toimplement, positioning it well to become a new default HPO method.

DEHB: Evolutionary Hyperband for Scalable, Robust and Efficient Hyperparameter Optimization