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

A key competence for open-ended learning is the formation of increasinglyabstract representations useful for driving complex behavior. Abstractrepresentations ignore specific details and facilitate generalization. Here weconsider the learning of abstract representations in a multi-modal setting withtwo or more input modalities. We treat the problem as a lossy compressionproblem and show that generic lossy compression of multimodal sensory inputnaturally extracts abstract representations that tend to strip away modalitiyspecific details and preferentially retain information that is shared acrossthe different modalities. Furthermore, we propose an architecture to learnabstract representations by identifying and retaining only the information thatis shared across multiple modalities while discarding any modality specificinformation.

Learning Abstract Representations through Lossy Compression of Multi-Modal Signals