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

By driving models to converge to flat minima, sharpness-aware learningalgorithms (such as SAM) have shown the power to achieve state-of-the-artperformances. However, these algorithms will generally incur one extraforward-backward propagation at each training iteration, which largely burdensthe computation especially for scalable models. To this end, we propose asimple yet efficient training scheme, called Randomized Sharpness-AwareTraining (RST). Optimizers in RST would perform a Bernoulli trial at eachiteration to choose randomly from base algorithms (SGD) and sharpness-awarealgorithms (SAM) with a probability arranged by a predefined schedulingfunction. Due to the mixture of base algorithms, the overall count ofpropagation pairs could be largely reduced. Also, we give theoretical analysison the convergence of RST. Then, we empirically study the computation cost andeffect of various types of scheduling functions, and give directions on settingappropriate scheduling functions. Further, we extend the RST to a generalframework (G-RST), where we can adjust regularization degree on sharpnessfreely for any scheduling function. We show that G-RST can outperform SAM inmost cases while saving 50\% extra computation cost.

Randomized Sharpness-Aware Training for Boosting Computational Efficiency in Deep Learning