Methods › General › Ensembling › EMEA
Entropy Minimized Ensemble of Adapters
EMEA
Introduced by Xinyi Wang et al. in Efficient Test Time Adapter Ensembling for Low-resource Language Varieties
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Entropy Minimized Ensemble of Adapters, or EMEA, is a method that optimizes the ensemble weights of the pretrained language adapters for each test sentence by minimizing the entropy of its predictions. The intuition behind the method is that a good adapter weight α for a test input x should make the model more confident in its prediction for x, that is, it should lead to lower model entropy over the input
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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Non-Parametric Online Learning from Human Feedback for Neural Machine Translation 23 Sep 2021 · 1 repository · arXiv:2109.11136Syntology ran 5 of 8 samples · 3 unverified · 8 pointer-only (licence)
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Efficient Test Time Adapter Ensembling for Low-resource Language Varieties 10 Sep 2021 · 1 repository · arXiv:2109.04877
Tasks archive 2025-07-28
10 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
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