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 consider a setting where a population of artificial learners is given, andthe objective is to optimize aggregate measures of performance, underconstraints on training resources. The problem is motivated by the study ofpeer learning in human educational systems. In this context, we study naturalknowledge diffusion processes in networks of interacting artificial learners.By `natural', we mean processes that reflect human peer learning where thestudents' internal state and learning process is mostly opaque, and the maindegree of freedom lies in the formation of peer learning groups by acoordinator who can potentially evaluate the learners before assigning them topeer groups. Among else, we empirically show that such processes indeed makeeffective use of the training resources, and enable the design of modularneural models that have the capacity to generalize without being prone tooverfitting noisy labels.

Resource-constrained knowledge diffusion processes inspired by human peer learning