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Switch FFN

20 papers tagged archive 2025-07-28

Introduced by William Fedus et al. in Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

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

A Switch FFN is a sparse layer that operates independently on tokens within an input sequence. It is shown in the blue block in the figure. We diagram two tokens (x₁ = “More” and x₂ = “Parameters” below) being routed (solid lines) across four FFN experts, where the router independently routes each token. The switch FFN layer returns the output of the selected FFN multiplied by the router gate value (dotted-line).

PaperSource

Papers archive 2025-07-28

20 shown of 20, 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
Mixture-of-Experts13
Language Modelling4
GPU3
Language Modeling3
Machine Translation3
Question Answering3
Text Generation2
Translation2
All1
Avg1
CPU1
Common Sense Reasoning1
Data Augmentation1
In-Context Learning1
Large Language Model1
Model Compression1
Neural Architecture Search1
Object Detection1
RAG1
Reinforcement Learning (RL)1

Usage over time archive 2025-07-28

Papers per year tagged with Switch FFN: 2021 to 2025, peak 7 7 0 2021: 5 papers 2021 2022: 7 papers 2022 2023: 5 papers 2023 2024: 0 papers 2024 2025: 3 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (20 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

Feedforward Networks

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