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• W2949662677 abstract "We propose a mixture-of-experts approach for unsupervised domain adaptation from multiple sources. The key idea is to explicitly capture the relationship between a target example and different source domains. This relationship, expressed by a point-to-set metric, determines how to combine predictors trained on various domains. The metric is learned in an unsupervised fashion using meta-training. Experimental results on sentiment analysis and part-of-speech tagging demonstrate that our approach consistently outperforms multiple baselines and can robustly handle negative transfer." @default.
• W2949662677 created "2019-06-27" @default.
• W2949662677 creator A5008575633 @default.
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• W2949662677 date "2018-09-06" @default.
• W2949662677 modified "2023-09-24" @default.
• W2949662677 title "Multi-Source Domain Adaptation with Mixture of Experts" @default.
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• W2949662677 doi "https://doi.org/10.48550/arxiv.1809.02256" @default.
• W2949662677 hasPublicationYear "2018" @default.
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