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In NIPS . 1135--1143."},{"key":"e_1_3_2_1_8_1","volume-title":"Data-driven sparse structure selection for deep neural networks. arXiv preprint arXiv:1707.01213","author":"Huang Zehao","year":"2017","unstructured":"Zehao Huang and Naiyan Wang . 2017. Data-driven sparse structure selection for deep neural networks. arXiv preprint arXiv:1707.01213 ( 2017 ). Zehao Huang and Naiyan Wang. 2017. Data-driven sparse structure selection for deep neural networks. arXiv preprint arXiv:1707.01213 (2017)."},{"key":"e_1_3_2_1_9_1","volume-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5MB model size. arXiv preprint","author":"Iandola Forrest N","year":"2016","unstructured":"Forrest N Iandola , Song Han , Matthew W Moskewicz , Khalid Ashraf , William J Dally , and Kurt Keutzer . 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5MB model size. arXiv preprint ( 2016 ). 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