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

A simple approach is proposed to obtain complexity controls for neuralnetworks with general activation functions. The approach is motivated byapproximating the general activation functions with one-dimensional ReLUnetworks, which reduces the problem to the complexity controls of ReLUnetworks. Specifically, we consider two-layer networks and deep residualnetworks, for which path-based norms are derived to control complexities. Wealso provide preliminary analyses of the function spaces induced by these normsand a priori estimates of the corresponding regularized estimators.

Complexity Measures for Neural Networks with General Activation Functions Using Path-based Norms