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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Nowadays, online learning is an appealing learning paradigm, which is ofgreat interest in practice due to the recent emergence of large scaleapplications such as online advertising placement and online web ranking.Standard online learning assumes a finite number of samples while in practicedata is streamed infinitely. In such a setting gradient descent with adiminishing learning rate does not work. We first introduce regret with rollingwindow, a new performance metric for online streaming learning, which measuresthe performance of an algorithm on every fixed number of contiguous samples. Atthe same time, we propose a family of algorithms based on gradient descent witha constant or adaptive learning rate and provide very technical analysesestablishing regret bound properties of the algorithms. We cover the convexsetting showing the regret of the order of the square root of the size of thewindow in the constant and dynamic learning rate scenarios. Our proof isapplicable also to the standard online setting where we provide the firstanalysis of the same regret order (the previous proofs have flaws). We alsostudy a two layer neural network setting with ReLU activation. In this case weestablish that if initial weights are close to a stationary point, the samesquare root regret bound is attainable. We conduct computational experimentsdemonstrating a superior performance of the proposed algorithms.

Convergence Analyses of Online ADAM Algorithm in Convex Setting and Two-Layer ReLU Neural Network