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Fastformer

4 papers tagged archive 2025-07-28

Introduced by Chuhan Wu et al. in Fastformer: Additive Attention Can Be All You Need

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

Fastformer is an type of Transformer which uses additive attention as a building block. Instead of modeling the pair-wise interactions between tokens, additive attention is used to model global contexts, and then each token representation is further transformed based on its interaction with global context representations.

PaperSource

Papers archive 2025-07-28

4 shown of 4, 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

12 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
News Recommendation2
All1
Image Segmentation1
Lesion Segmentation1
Mamba1
Medical Image Segmentation1
Segmentation1
Self-Supervised Learning1
Semantic Segmentation1
Skin Lesion Segmentation1
Text Classification1
Text Summarization1

Usage over time archive 2025-07-28

Papers per year tagged with Fastformer: 2021 to 2025, peak 1 1 0 2021: 1 paper 2021 2022: 1 paper 2022 2023: 0 papers 2023 2024: 1 paper 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (4 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

Transformers

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