EMEA

2000년 도입 · 논문 2편에서 사용

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 $\alpha$ 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

출처: Efficient Test Time Adapter Ensembling for Low-resource Language Varieties

소개 논문: Efficient Test Time Adapter Ensembling for Low-resource Language Varieties