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

Patterns stored within pre-trained deep neural networks compose large andpowerful descriptive languages that can be used for many different purposes.Typically, deep network representations are implemented within vector embeddingspaces, which enables the use of traditional machine learning algorithms on topof them. In this short paper we propose the construction of a graph embeddingspace instead, introducing a methodology to transform the knowledge codedwithin a deep convolutional network into a topological space (i.e. a network).We outline how such graph can hold data instances, data features, relationsbetween instances and features, and relations among features. Finally, weintroduce some preliminary experiments to illustrate how the resultant graphembedding space can be exploited through graph analytics algorithms.

Building Graph Representations of Deep Vector Embeddings