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Found 2 papers, 1 papers with code
Reinforced Training Data Selection for Domain Adaptation
Supervised models suffer from the problem of domain shifting where distribution mismatch in the data across domains greatly affect model performance.
DOMAIN ADAPTATION VIA DISTRIBUTION AND REPRESENTATION MATCHING: A CASE STUDY ON TRAINING DATA SELECTION VIA REINFORCEMENT LEARNING
Following the TDS methodology, in this paper, we propose a general data selection framework with representation learning and distribution matching simultaneously for domain adaptation on neural models.
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