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 propose a set of denoising neural networks capable of operating on anarbitrary number of channels at runtime, irrespective of how many channels theywere trained on. We coin the proposed models multi-view networks since theyoperate using multiple views of the same data. We explore two sucharchitectures and show how they outperform traditional denoising models inmulti-channel scenarios. Additionally, we demonstrate how multi-view networkscan leverage information provided by additional recordings to make betterpredictions, and how they are able to generalize to a number of recordings notseen in training.

Multi-View Networks for Denoising of Arbitrary Numbers of Channels