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 learn about the world from a diverse range of sensory information.Automated systems lack this ability as investigation has centred on processinginformation presented in a single form. Adapting architectures to learn frommultiple modalities creates the potential to learn rich representations of theworld - but current multimodal systems only deliver marginal improvements onunimodal approaches. Neural networks learn sampling noise during training withthe result that performance on unseen data is degraded. This researchintroduces a second objective over the multimodal fusion process learned withvariational inference. Regularisation methods are implemented in the innertraining loop to control variance and the modular structure stabilisesperformance as additional neurons are added to layers. This framework isevaluated on a multilabel classification task with textual and visual inputs todemonstrate the potential for multiple objectives and probabilistic methods tolower variance and improve generalisation.

Training Multimodal Systems for Classification with Multiple Objectives