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

In this work, we study an optimizer, Grad-Avg to optimize error functions. Weestablish the convergence of the sequence of iterates of Grad-Avgmathematically to a minimizer (under boundedness assumption). We apply Grad-Avgalong with some of the popular optimizers on regression as well asclassification tasks. In regression tasks, it is observed that the behaviour ofGrad-Avg is almost identical with Stochastic Gradient Descent (SGD). We presenta mathematical justification of this fact. In case of classification tasks, itis observed that the performance of Grad-Avg can be enhanced by suitablyscaling the parameters. Experimental results demonstrate that Grad-Avgconverges faster than the other state-of-the-art optimizers for theclassification task on two benchmark datasets.

A Variant of Gradient Descent Algorithm Based on Gradient Averaging