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OpenReview.net."},{"key":"e_1_3_2_1_95_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2205.12141"},{"key":"e_1_3_2_1_96_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2017.09.005"},{"key":"e_1_3_2_1_97_1","volume-title":"Li","author":"Yi Dong","year":"2014","unstructured":"Dong Yi, Zhen Lei, Shengcai Liao, and Stan Z. Li. 2014. Learning Face Representation from Scratch. CoRR abs\/1411.7923 (2014). arXiv:1411.7923 http:\/\/arxiv.org\/abs\/1411.7923"},{"key":"e_1_3_2_1_98_1","volume-title":"Indiscriminate Poisoning Attacks Are Shortcuts. CoRR abs\/2111.00898","author":"Yu Da","year":"2021","unstructured":"Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, and Tie-Yan Liu. 2021. Indiscriminate Poisoning Attacks Are Shortcuts. 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