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

While existing work on neural architecture search (NAS) tunes hyperparametersin a separate post-processing step, we demonstrate that architectural choicesand other hyperparameter settings interact in a way that can render thisseparation suboptimal. Likewise, we demonstrate that the common practice ofusing very few epochs during the main NAS and much larger numbers of epochsduring a post-processing step is inefficient due to little correlation in therelative rankings for these two training regimes. To combat both of theseproblems, we propose to use a recent combination of Bayesian optimization andHyperband for efficient joint neural architecture and hyperparameter search.

Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search