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

How can we efficiently mitigate the overhead of gradient communications indistributed optimization? This problem is at the heart of training scalablemachine learning models and has been mainly studied in the unconstrainedsetting. In this paper, we propose Quantized-Frank-Wolfe (QFW), the firstprojection-free and communication-efficient algorithm for solving constrainedoptimization problems at scale. We consider both convex and non-convexobjective functions, expressed as a finite-sum or more generally a stochasticoptimization problem, and provide strong theoretical guarantees on theconvergence rate of QFW. This is accomplished by proposing novel quantizationschemes that efficiently compress gradients while controlling the noisevariance introduced during this process. Finally, we empirically validate theefficiency of QFW in terms of communication and the quality of returnedsolution against natural baselines.

Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free