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/

Zendy Plus

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

Zendy Tools

Zendy Open

The attention score matrix ${\rm SoftMax}(XY^T)$ encodes relationalsimilarity patterns between objects and is extremely popular in machinelearning. However, the complexity required to calculate it runs quadraticallywith the problem size, making it a computationally heavy solution. In thisarticle, we propose a linear-time approximation of the attention scorenormalization constants for embedding vectors with bounded norms. We show onseveral pre-trained embeddings that the accuracy of our estimation formulasurpasses competing kernel methods by even orders of magnitude. From thisresult, we design a linear-time and task-agnostic embedding algorithm based onthe optimization of the attention scores. The proposed algorithm is highlyinterpretable and easily adapted to an arbitrary embedding problem. We considera few use-cases and observe similar or higher performances and a lowercomputational time with respect to comparable embedding algorithms.

Efficient distributed representations with linear-time attention scores normalization