Methods › Natural Language Processing › Autoregressive Transformers › Linformer

Linformer

17 papers tagged archive 2025-07-28

Introduced by Sinong Wang et al. in Linformer: Self-Attention with Linear Complexity

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

Linformer is a linear Transformer that utilises a linear self-attention mechanism to tackle the self-attention bottleneck with Transformer models. The original scaled dot-product attention is decomposed into multiple smaller attentions through linear projections, such that the combination of these operations forms a low-rank factorization of the original attention.

PaperSource

Papers archive 2025-07-28

17 shown of 17, 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

20 shown of 30 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
Language Modelling3
GPU2
Knowledge Distillation2
Language Modeling2
Survey2
Abstractive Text Summarization1
Autonomous Driving1
Classification1
Data Augmentation1
Deblurring1
Depth Estimation1
Image Classification1
Image Generation1
Image Restoration1
Inductive Bias1
Machine Translation1
Mamba1
Navigate1
Neural Architecture Search1
Position1

Usage over time archive 2025-07-28

Papers per year tagged with Linformer: 2020 to 2025, peak 6 6 0 2020: 3 papers 2020 2021: 6 papers 2021 2022: 3 papers 2022 2023: 1 paper 2023 2024: 3 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (17 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

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