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

One of the major problems in modeling natural signals is that signals withvery similar structure may locally have completely different measurements,e.g., images taken under different illumination conditions, or the speechsignal captured in different environments. While there have been manysuccessful attempts to address these problems in application-specific settings,we believe that underlying a large set of problems in signal representation isa representational deficiency of intensity-derived local measurements that arethe basis of most efficient models. We argue that interesting structure insignals is better captured when the signal is de- fined as a matrix whoseentries are discrete indices to a separate palette of possible measurements. Inorder to model the variability in signal structure, we define a signal classnot by a single index map, but by a probability distribution over the indexmaps, which can be estimated from the data, and which we call probabilisticindex maps. The existing algorithm can be adapted to work with thisrepresentation. Furthermore, the probabilistic index map representation leadsto algorithms with computational costs proportional to either the size of thepalette or the log of the size of the palette, making the cost of significantlyincreased invariance to non-structural changes quite bearable. We illustratethe benefits of the probabilistic index map representation in severalapplications in computer vision and speech processing.

Probabilistic index maps for modeling natural signals