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/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Given an n x n matrix A, we present a simple, element-wise sparsificationalgorithm that zeroes out all sufficiently small elements of A and then retainssome of the remaining elements with probabilities proportional to the square oftheir magnitudes. We analyze the approximation accuracy of the proposedalgorithm using a recent, elegant non-commutative Bernstein inequality, andcompare our bounds with all existing (to the best of our knowledge)element-wise matrix sparsification algorithms.

A Note on Element-wise Matrix Sparsification via a Matrix-valued Bernstein Inequality