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Residual Shuffle-Exchange Network

RSE

5 papers tagged archive 2025-07-28

Introduced by Andis Draguns et al. in Residual Shuffle-Exchange Networks for Fast Processing of Long Sequences

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

Residual Shuffle-Exchange Network is an efficient alternative to models using an attention mechanism that allows the modelling of long-range dependencies in sequences in O(n log n) time. This model achieved state-of-the-art performance on the MusicNet dataset for music transcription while being able to run inference on a single GPU fast enough to be suitable for real-time audio processing.

PaperSource

Papers archive 2025-07-28

5 shown of 5, 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
State Estimation2
LAMBADA1
Language Modelling1
Matrix Completion1
Music Transcription1
Q-Learning1
Retrieval1

Usage over time archive 2025-07-28

Papers per year tagged with RSE: 2020 to 2025, peak 2 2 0 2020: 2 papers 2020 2021: 0 papers 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 (5 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

Music Transcription

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