Methods › General › Attention Modules › All-Attention Layer

All-Attention Layer

2 papers tagged archive 2025-07-28

Introduced by Sainbayar Sukhbaatar et al. in Augmenting Self-attention with Persistent Memory

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

An All-Attention Layer is an attention module and layer for transformers that merges the self-attention and feedforward sublayers into a single unified attention layer. As opposed to the two-step mechanism of the Transformer layer, it directly builds its representation from the context and a persistent memory block without going through a feedforward transformation. The additional persistent memory block stores, in the form of key-value vectors, information that does not depend on the context. In terms of parameters, these persistent key-value vectors replace the feedforward sublayer.

PaperSource

Papers archive 2025-07-28

2 shown of 2, 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

7 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
Deep Reinforcement Learning1
Language Modeling1
Language Modelling1
Reinforcement Learning1
Translation1
Traveling Salesman Problem1
reinforcement-learning1

Usage over time archive 2025-07-28

Papers per year tagged with All-Attention Layer: 2019 to 2023, peak 1 1 0 2019: 1 paper 2019 2020: 0 papers 2020 2021: 0 papers 2021 2022: 0 papers 2022 2023: 1 paper 2023
Papers per year the archive tags with this method, by the paper's archive date (2 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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