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Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation. In ICML. 7124--7133. Kaichao You Ximei Wang Mingsheng Long and Michael I. Jordan. 2019. Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation. In ICML. 7124--7133."},{"key":"e_1_3_2_2_67_1","unstructured":"Chris Zhang Mengye Ren and Raquel Urtasun. 2019 b. Graph HyperNetworks for Neural Architecture Search. In ICLR. Chris Zhang Mengye Ren and Raquel Urtasun. 2019 b. Graph HyperNetworks for Neural Architecture Search. In ICLR."},{"key":"e_1_3_2_2_68_1","unstructured":"Linfeng Zhang Jiebo Song Anni Gao Jingwei Chen Chenglong Bao and Kaisheng Ma. [n. d.]. Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation. CoRR ([n. d.]). Linfeng Zhang Jiebo Song Anni Gao Jingwei Chen Chenglong Bao and Kaisheng Ma. [n. d.]. Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation. CoRR ([n. d.])."},{"key":"e_1_3_2_2_69_1","unstructured":"Yuchen Zhang Tianle Liu Mingsheng Long and Michael I. Jordan. 2019 a. Bridging Theory and Algorithm for Domain Adaptation. In ICML. 7404--7413. Yuchen Zhang Tianle Liu Mingsheng Long and Michael I. Jordan. 2019 a. Bridging Theory and Algorithm for Domain Adaptation. In ICML. 7404--7413."},{"key":"e_1_3_2_2_70_1","unstructured":"Han Zhao Remi Tachet des Combes Kun Zhang and Geoffrey J. Gordon. 2019. On Learning Invariant Representations for Domain Adaptation. In ICML. 7523--7532. Han Zhao Remi Tachet des Combes Kun Zhang and Geoffrey J. Gordon. 2019. On Learning Invariant Representations for Domain Adaptation. 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