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

Pareto Front Learning (PFL) was recently introduced as an efficient methodfor approximating the entire Pareto front, the set of all optimal solutions toa Multi-Objective Optimization (MOO) problem. In the previous work, the mappingbetween a preference vector and a Pareto optimal solution is still ambiguous,rendering its results. This study demonstrates the convergence and completionaspects of solving MOO with pseudoconvex scalarization functions and combinesthem into Hypernetwork in order to offer a comprehensive framework for PFL,called Controllable Pareto Front Learning. Extensive experiments demonstratethat our approach is highly accurate and significantly less computationallyexpensive than prior methods in term of inference time.

A Framework for Controllable Pareto Front Learning with Completed Scalarization Functions and its Applications