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

This work introduces TrimTuner, the first system for optimizing machinelearning jobs in the cloud to exploit sub-sampling techniques to reduce thecost of the optimization process while keeping into account user-specifiedconstraints. TrimTuner jointly optimizes the cloud and application-specificparameters and, unlike state of the art works for cloud optimization, eschewsthe need to train the model with the full training set every time a newconfiguration is sampled. Indeed, by leveraging sub-sampling techniques anddata-sets that are up to 60x smaller than the original one, we show thatTrimTuner can reduce the cost of the optimization process by up to 50x.Further, TrimTuner speeds-up the recommendation process by 65x with respect tostate of the art techniques for hyper-parameter optimization that usesub-sampling techniques. The reasons for this improvement are twofold: i) anovel domain specific heuristic that reduces the number of configurations forwhich the acquisition function has to be evaluated; ii) the adoption of anensemble of decision trees that enables boosting the speed of therecommendation process by one additional order of magnitude.

TrimTuner: Efficient Optimization of Machine Learning Jobs in the Cloud via Sub-Sampling