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

Stochastic Gradient Descent (SGD) and its variants are the most usedalgorithms in machine learning applications. In particular, SGD with adaptivelearning rates and momentum is the industry standard to train deep networks.Despite the enormous success of these methods, our theoretical understanding ofthese variants in the nonconvex setting is not complete, with most of theresults only proving convergence in expectation and with strong assumptions onthe stochastic gradients. In this paper, we present a high probability analysisfor adaptive and momentum algorithms, under weak assumptions on the function,stochastic gradients, and learning rates. We use it to prove for the first timethe convergence of the gradients to zero in high probability in the smoothnonconvex setting for Delayed AdaGrad with momentum.

A High Probability Analysis of Adaptive SGD with Momentum