Methods › Natural Language Processing › Transformers › Subformer
Subformer
Introduced by Machel Reid et al. in Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Subformer is a Transformer that combines sandwich-style parameter sharing, which overcomes naive cross-layer parameter sharing in generative models, and self-attentive embedding factorization (SAFE). In SAFE, a small self-attention layer is used to reduce embedding parameter count.
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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Transformers are efficient hierarchical chemical graph learners 2 Oct 2023 · 1 repository · arXiv:2310.01704
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Subformer: A Parameter Reduced Transformer 1 Jan 2021 · 0 repositories
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Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers 1 Jan 2021 · 1 repository · arXiv:2101.00234
Tasks archive 2025-07-28
8 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Abstractive Text Summarization | 2 |
| Language Modeling | 2 |
| Language Modelling | 2 |
| Machine Translation | 2 |
| Translation | 2 |
| Decoder | 1 |
| Graph Representation Learning | 1 |
| Representation Learning | 1 |
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