Methods › Computer Vision › Image Feature Extractors › Hamburger
Hamburger
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Hamburger is a global context module that employs matrix decomposition to factorize the learned representation into sub-matrices so as to recover the clean low-rank signal subspace. The key idea is, if we formulate the inductive bias like the global context into an objective function, the optimization algorithm to minimize the objective function can construct a computational graph, i.e., the architecture we need in the networks.
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.
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HAMburger: Accelerating LLM Inference via Token Smashing 26 May 2025 · 0 repositories · arXiv:2505.20438
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Is Attention Better Than Matrix Decomposition? 9 Sep 2021 · 3 repositories · arXiv:2109.04553Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
Tasks archive 2025-07-28
4 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 |
|---|---|
| Conditional Image Generation | 1 |
| Image Generation | 1 |
| Large Language Model | 1 |
| Semantic Segmentation | 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