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

Lifted Relational Neural Networks (LRNNs) describe relational domains usingweighted first-order rules which act as templates for constructing feed-forwardneural networks. While previous work has shown that using LRNNs can lead tostate-of-the-art results in various ILP tasks, these results depended onhand-crafted rules. In this paper, we extend the framework of LRNNs withstructure learning, thus enabling a fully automated learning process. Similarlyto many ILP methods, our structure learning algorithm proceeds in an iterativefashion by top-down searching through the hypothesis space of all possible Hornclauses, considering the predicates that occur in the training examples as wellas invented soft concepts entailed by the best weighted rules found so far. Inthe experiments, we demonstrate the ability to automatically induce usefulhierarchical soft concepts leading to deep LRNNs with a competitive predictivepower.

Stacked Structure Learning for Lifted Relational Neural Networks