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

We propose a theoretical framework for an adaptive learning rate policy forthe Mean Absolute Error loss function and Quantile loss function and evaluateits effectiveness for regression tasks. The framework is based on the theory ofLipschitz continuity, specifically utilizing the relationship between learningrate and Lipschitz constant of the loss function. Based on experimentation, wehave found that the adaptive learning rate policy enables up to 20x fasterconvergence compared to a constant learning rate policy.

LALR: Theoretical and Experimental validation of Lipschitz Adaptive Learning Rate in Regression and Neural Networks