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Low-Rank Factorization-based Multi-Head Attention

LAMA

42 papers tagged archive 2025-07-28

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

Low-Rank Factorization-based Multi-head Attention Mechanism, or LAMA, is a type of attention module that uses low-rank factorization to reduce computational complexity. It uses low-rank bilinear pooling to construct a structured sentence representation that attends to multiple aspects of a sentence.

Source: Low Rank Factorization for Compact Multi-Head Self-AttentionSee Code · JohnGiorgi/compact-multi-head-self-attention-pytorch

Papers archive 2025-07-28

30 shown of 42, 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 56 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 Modelling11
Language Modeling10
Image Inpainting4
In-Context Learning3
Inductive Bias3
Knowledge Probing3
Question Answering3
Text Classification3
model3
text-classification3
Masked Language Modeling2
Meta-Learning2
Object2
Retrieval2
SSIM2
Sentiment Analysis2
4k1
Anomaly Detection1
Articles1
Benchmarking1

Usage over time archive 2025-07-28

Papers per year tagged with LAMA: 2019 to 2025, peak 14 14 0 2019: 1 paper 2019 2020: 4 papers 2020 2021: 14 papers 2021 2022: 9 papers 2022 2023: 6 papers 2023 2024: 6 papers 2024 2025: 2 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (42 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

Attention Modules

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