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 new stochastic gradient method for optimizing the sum of afinite set of smooth functions, where the sum is strongly convex. Whilestandard stochastic gradient methods converge at sublinear rates for thisproblem, the proposed method incorporates a memory of previous gradient valuesin order to achieve a linear convergence rate. In a machine learning context,numerical experiments indicate that the new algorithm can dramaticallyoutperform standard algorithms, both in terms of optimizing the training errorand reducing the test error quickly.

A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets