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

Deep Belief Networks (DBN) have been successfully applied on popular machinelearning tasks. Specifically, when applied on hand-written digit recognition,DBNs have achieved approximate accuracy rates of 98.8%. In an effort tooptimize the data representation achieved by the DBN and maximize theirdescriptive power, recent advances have focused on inducing sparse constraintsat each layer of the DBN. In this paper we present a theoretical approach forsparse constraints in the DBN using the mixed norm for both non-overlapping andoverlapping groups. We explore how these constraints affect the classificationaccuracy for digit recognition in three different datasets (MNIST, USPS, RIMES)and provide initial estimations of their usefulness by altering differentparameters such as the group size and overlap percentage.

Sparse Penalty in Deep Belief Networks: Using the Mixed Norm Constraint