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

Transformer is a powerful architecture that achieves superior performance onvarious sequence learning tasks, including neural machine translation, languageunderstanding, and sequence prediction. At the core of the Transformer is theattention mechanism, which concurrently processes all inputs in the streams. Inthis paper, we present a new formulation of attention via the lens of thekernel. To be more precise, we realize that the attention can be seen asapplying kernel smoother over the inputs with the kernel scores being thesimilarities between inputs. This new formulation gives us a better way tounderstand individual components of the Transformer's attention, such as thebetter way to integrate the positional embedding. Another important advantageof our kernel-based formulation is that it paves the way to a larger space ofcomposing Transformer's attention. As an example, we propose a new variant ofTransformer's attention which models the input as a product of symmetrickernels. This approach achieves competitive performance to the current state ofthe art model with less computation. In our experiments, we empirically studydifferent kernel construction strategies on two widely used tasks: neuralmachine translation and sequence prediction.

Transformer Dissection: A Unified Understanding of Transformer's Attention via the Lens of Kernel