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

Many machine learning tasks such as multiple instance learning, 3D shaperecognition, and few-shot image classification are defined on sets ofinstances. Since solutions to such problems do not depend on the order ofelements of the set, models used to address them should be permutationinvariant. We present an attention-based neural network module, the SetTransformer, specifically designed to model interactions among elements in theinput set. The model consists of an encoder and a decoder, both of which relyon attention mechanisms. In an effort to reduce computational complexity, weintroduce an attention scheme inspired by inducing point methods from sparseGaussian process literature. It reduces the computation time of self-attentionfrom quadratic to linear in the number of elements in the set. We show that ourmodel is theoretically attractive and we evaluate it on a range of tasks,demonstrating the state-of-the-art performance compared to recent methods forset-structured data.

Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks