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 use of blackbox solvers inside neural networks is a relatively new areawhich aims to improve neural network performance by including proven, efficientsolvers for complex problems. Existing work has created methods for learningnetworks with these solvers as components while treating them as a blackbox.This work attempts to improve upon existing techniques by optimizing not onlyover the primary loss function, but also over the performance of the solveritself by using Time-cost Regularization. Additionally, we propose a method tolearn blackbox parameters such as which blackbox solver to use or the heuristicfunction for a particular solver. We do this by introducing the idea of ahyper-blackbox which is a blackbox around one or more internal blackboxes.

Learning and Optimization of Blackbox Combinatorial Solvers in Neural Networks