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Yang, End-to-end adversarial memory network for cross-domain sentiment classification , in: Proceedings of the 26th International Joint Conference on Artificial Intelligence , 2017 , pp. 2237\u2013 2243 . [7] Z. Li, Y. Zhang, Y. Wei, Y. Wu, Q. Yang, End-to-end adversarial memory network for cross-domain sentiment classification, in: Proceedings of the 26th International Joint Conference on Artificial Intelligence, 2017, pp. 2237\u20132243."},{"key":"e_1_3_2_1_8_1","volume-title":"Lempitsky","author":"Ganin","year":"2016","unstructured":"[ 8 ] Y aroslav Ganin , Evgeniya Ustinova, Hana Ajakan , Pas-cal Germain, Hugo Larochelle , Francois Laviolette, Mario Marchand , and Victor S . Lempitsky . 2016 .Domain-adversarial training of neural networks. J.Mach. Learn. Res . [8] Y aroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pas-cal Germain, Hugo Larochelle, Francois Laviolette,Mario Marchand, and Victor S. Lempitsky. 2016.Domain-adversarial training of neural networks. J.Mach. Learn. 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Image-based recommendations on styles and substitutes. In SIGIR 43\u201352","DOI":"10.1145\/2766462.2767755"},{"key":"e_1_3_2_1_13_1","first-page":"1014","article-title":"Learning semantic representations of users and products for document level sentiment classification","volume":"1","author":"Tang D.","year":"2015","unstructured":"[ 13 ] Tang , D. ; Qin , B. ; and Liu , T. 2015 b. Learning semantic representations of users and products for document level sentiment classification . In ACL , volume 1 , 1014 \u2013 1023 . [13] Tang, D.; Qin, B.; and Liu, T. 2015b. Learning semantic representations of users and products for document level sentiment classification. In ACL, volume 1, 1014\u20131023.","journal-title":"ACL"},{"key":"e_1_3_2_1_14_1","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"Gretton A.","year":"2012","unstructured":"[ 14 ] Gretton , A. ; Borgwardt , K. M. ; Rasch , M. J. ; Sch\u00f6lkopf , B. ; and Smola , A. 2012 a. A kernel two-sample test . JMLR 13 (Mar): 723 \u2013 773 . [14] Gretton, A.; Borgwardt, K. M.; Rasch, M. J.; Sch\u00f6lkopf, B.; and Smola, A. 2012a. A kernel two-sample test. JMLR 13(Mar):723\u2013773.","journal-title":"JMLR"},{"key":"e_1_3_2_1_15_1","unstructured":"[\n  15\n  ]  Shen J.; Qu Y.; Zhang W.; and Y u Y. 2017. Wasserstein distance guided representation learning for domain adaptation. arXiv preprint arXiv:1707.01217.  [15] Shen J.; Qu Y.; Zhang W.; and Y u Y. 2017. Wasserstein distance guided representation learning for domain adaptation. arXiv preprint arXiv:1707.01217."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"[\n  16\n  ]  He R.; Lee W. S.; Ng H. T.; and Dahlmeier D. 2018. Adaptive semi-supervised learning for cross-domain sentiment classification. In EMNLP 3467\u20133476.  [16] He R.; Lee W. S.; Ng H. T.; and Dahlmeier D. 2018. Adaptive semi-supervised learning for cross-domain sentiment classification. 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