Methods › Natural Language Processing › Autoregressive Transformers › Sinkhorn Transformer

Sinkhorn Transformer

1 paper tagged archive 2025-07-28

Introduced by Yi Tay et al. in Sparse Sinkhorn Attention

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

The Sinkhorn Transformer is a type of transformer that uses Sparse Sinkhorn Attention as a building block. This component is a plug-in replacement for dense fully-connected attention (as well as local attention, and sparse attention alternatives), and allows for reduced memory complexity as well as sparse attention.

PaperSource

Papers archive 2025-07-28

1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Document Classification1
Image Generation1
Language Modeling1
Language Modelling1
Natural Language Inference1

Usage over time archive 2025-07-28

Papers per year tagged with Sinkhorn Transformer: 2020 to 2020, peak 1 1 0 2020: 1 paper 2020
Papers per year the archive tags with this method, by the paper's archive date (1 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Autoregressive TransformersTransformers

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections