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

The great success of deep learning heavily relies on increasingly largertraining data, which comes at a price of huge computational and infrastructuralcosts. This poses crucial questions that, do all training data contribute tomodel's performance? How much does each individual training sample or asub-training-set affect the model's generalization, and how to construct thesmallest subset from the entire training data as a proxy training set withoutsignificantly sacrificing the model's performance? To answer these, we proposedataset pruning, an optimization-based sample selection method that can (1)examine the influence of removing a particular set of training samples onmodel's generalization ability with theoretical guarantee, and (2) constructthe smallest subset of training data that yields strictly constrainedgeneralization gap. The empirically observed generalization gap of datasetpruning is substantially consistent with our theoretical expectations.Furthermore, the proposed method prunes 40% training examples on the CIFAR-10dataset, halves the convergence time with only 1.3% test accuracy decrease,which is superior to previous score-based sample selection methods.

Dataset Pruning: Reducing Training Data by Examining Generalization Influence