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Visualizing and Understanding Convolutional Networks. In ECCV , , David J. Fleet, Tom\u00e1 s Pajdla, Bernt Schiele, and Tinne Tuytelaars (Eds.), Vol. 8689. 818--833."},{"key":"e_1_3_2_2_76_1","doi-asserted-by":"crossref","unstructured":"Ce Zhang Arun Kumar and Christopher R\u00e9. 2014. Materialization Optimizations for Feature Selection Workloads. In SIGMOD. 265--276.  Ce Zhang Arun Kumar and Christopher R\u00e9. 2014. Materialization Optimizations for Feature Selection Workloads. In SIGMOD. 265--276.","DOI":"10.1145\/2588555.2593678"},{"key":"e_1_3_2_2_77_1","volume-title":"Ungar","author":"Zhou Jing","year":"2005","unstructured":"Jing Zhou , Dean P. Foster , Robert A. Stine , and Lyle H . Ungar . 2005 . Streaming Feature Selection using Alpha-investing. In SIGKDD. 384--393. Jing Zhou, Dean P. Foster, Robert A. Stine, and Lyle H. Ungar. 2005. Streaming Feature Selection using Alpha-investing. 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