Methods › Natural Language Processing › Autoregressive Transformers › Routing Transformer
Routing Transformer
Introduced by Aurko Roy et al. in Efficient Content-Based Sparse Attention with Routing Transformers
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
The Routing Transformer is a Transformer that endows self-attention with a sparse routing module based on online k-means. Each attention module considers a clustering of the space: the current timestep only attends to context belonging to the same cluster. In other word, the current time-step query is routed to a limited number of context through its cluster assignment.
Papers archive 2025-07-28
3 shown of 3, 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.
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Hybrid Routing Transformer for Zero-Shot Learning 29 Mar 2022 · 0 repositories · arXiv:2203.15310
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Hurdles to Progress in Long-form Question Answering 10 Mar 2021 · 2 repositories · arXiv:2103.06332
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Efficient Content-Based Sparse Attention with Routing Transformers 12 Mar 2020 · 2 repositories · arXiv:2003.05997Syntology ran 3 of 3 samples · 0 unverified · 2 pointer-only (licence)
Tasks archive 2025-07-28
12 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
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
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections