89 citations · 115 across the 5 of their papers we have counts for
13 papers
Fisher Deep Domain Adaptation
Yinghua Zhang, Yu Zhang, Ying Wei +3
Deep domain adaptation models learn a neural network in an unlabeled target domain by leveraging the knowledge from a labeled source domain. This can be achieved by learning a doma…
Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning
Zheng Li, Xin Li, Ying Wei +3
Joint extraction of aspects and sentiments can be effectively formulated as a sequence labeling problem. However, such formulation hinders the effectiveness of supervised methods d…
Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text
Guangneng Hu, Yu Zhang, Qiang Yang
Collaborative filtering (CF) is the key technique for recommender systems (RSs). CF exploits user-item behavior interactions (e.g., clicks) only and hence suffers from the data spa…
Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification
Zheng Li, Ying Wei, Yu Zhang +3
Aspect-level sentiment classification (ASC) aims at identifying sentiment polarities towards aspects in a sentence, where the aspect can behave as a general Aspect Category (AC) or…
Learning to Multitask
Yu Zhang, Ying Wei, Qiang Yang
Multitask learning has shown promising performance in many applications and many multitask models have been proposed. In order to identify an effective multitask model for a given…
Parameter Transfer Unit for Deep Neural Networks
Yinghua Zhang, Yu Zhang, Qiang Yang
Parameters in deep neural networks which are trained on large-scale databases can generalize across multiple domains, which is referred as "transferability". Unfortunately, the tra…