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Le Mohammad Norouzi Wolfgang Macherey Maxim Krikun Yuan Cao Qin Gao Klaus Macherey Jeff Klingner Apurva Shah Melvin Johnson Xiaobing Liu Lukasz Kaiser Stephan Gouws Yoshikiyo Kato Taku Kudo Hideto Kazawa Keith Stevens George Kurian Nishant Patil Wei Wang Cliff Young Jason Smith Jason Riesa Alex Rudnick Oriol Vinyals Greg Corrado Macduff Hughes and Jeffrey Dean. 2016. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. arXiv:1609.08144."},{"key":"e_1_3_2_1_73_1","unstructured":"Jungang Yang Liyao Xiang Weiting Li Wei Liu and Xinbing Wang. 2021. Improved Matrix Gaussian Mechanism for Differential Privacy. arXiv:2104.14808."},{"key":"e_1_3_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3093316"},{"key":"e_1_3_2_1_75_1","doi-asserted-by":"crossref","unstructured":"Samuel Yeom Irene Giacomelli Matt Fredrikson and Somesh Jha. 2018. Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting. 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