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Representation learning via an integrated autoencoder for unsupervised domain adaptation被引量：2
《Frontiers of Computer Science》2023年第5期75-87,共13页Yi ZHU Xindong WU Jipeng QIANG Yunhao YUAN Yun LI 
supported by the National Natural Science Foundation of China(Grant Nos.61906060,62076217,62120106008);the Yangzhou University Interdisciplinary Research Foundation for Animal Husbandry Discipline of Targeted Support(yzuxk202015);the Opening Foundation of Key Laboratory of Huizhou Architecture in Anhui Province(HPJZ-2020-02);the Open Project Program of Joint International Research Laboratory of Agriculture and AgriProduct Safety(JILAR-KF202104).
The purpose of unsupervised domain adaptation is to use the knowledge of the source domain whose data distribution is different from that of the target domain for promoting the learning task in the target domain.The k...
关键词：unsupervised domain adaptation representation learning marginalized autoencoder convolutional autoen-coder sparse autoencoder 
Improving neural sentence alignment with word translation被引量：2
《Frontiers of Computer Science》2021年第1期81-90,共10页Ying DING Junhui LI Zhengxian GONG Guodong ZHOU 
This work was supported by the National Natural Science Foundation of China(Grant Nos.61876120,61673290).
Sentence alignment is a basic task in natural lan-guage processing which aims to extract high-quality paral-lel sentences automatically.Motivated by the observation that aligned sentence pairs contain a larger number ...
关键词：sentence alignment word translation mixeden coder factored encoder gated encoder 
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