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

The discovery of neural architectures from simple building blocks is along-standing goal of Neural Architecture Search (NAS). Hierarchical searchspaces are a promising step towards this goal but lack a unifying search spacedesign framework and typically only search over some limited aspect ofarchitectures. In this work, we introduce a unifying search space designframework based on context-free grammars that can naturally and compactlygenerate expressive hierarchical search spaces that are 100s of orders ofmagnitude larger than common spaces from the literature. By enhancing and usingtheir properties, we effectively enable search over the complete architectureand can foster regularity. Further, we propose an efficient hierarchical kerneldesign for a Bayesian Optimization search strategy to efficiently search oversuch huge spaces. We demonstrate the versatility of our search space designframework and show that our search strategy can be superior to existing NASapproaches. Code is available athttps://github.com/automl/hierarchical_nas_construction.

Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammars