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

Robust perception relies on both bottom-up and top-down signals. Bottom-upsignals consist of what's directly observed through sensation. Top-down signalsconsist of beliefs and expectations based on past experience and short-termmemory, such as how the phrase `peanut butter and~...' will be completed. Theoptimal combination of bottom-up and top-down information remains an openquestion, but the manner of combination must be dynamic and both context andtask dependent. To effectively utilize the wealth of potential top-downinformation available, and to prevent the cacophony of intermixed signals in abidirectional architecture, mechanisms are needed to restrict information flow.We explore deep recurrent neural net architectures in which bottom-up andtop-down signals are dynamically combined using attention. Modularity of thearchitecture further restricts the sharing and communication of information.Together, attention and modularity direct information flow, which leads toreliable performance improvements in perceptual and language tasks, and inparticular improves robustness to distractions and noisy data. We demonstrateon a variety of benchmarks in language modeling, sequential imageclassification, video prediction and reinforcement learning that the\emph{bidirectional} information flow can improve results over strongbaselines.

Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules