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

We introduce CQnet, a neural network with origins in the CQ algorithm forsolving convex split-feasibility problems and forward-backward splitting.CQnet's trajectories are interpretable as particles that are tracking achanging constraint set via its point-to-set distance function while beingelements of another constraint set at every layer. More than just aconvex-geometric interpretation, CQnet accommodates learned and deterministicconstraints that may be sample or data-specific and are satisfied by everylayer and the output. Furthermore, the states in CQnet progress toward anotherconstraint set at every layer. We provide proof of stability/nonexpansivenesswith minimal assumptions. The combination of constraint handling and stabilityput forward CQnet as a candidate for various tasks where prior knowledge existson the network states or output.

CQnet: convex-geometric interpretation and constraining neural-network trajectories