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Dally, \u201cLearning both weights and connections for efficient neural network,\u201d Neural Information Processing Systems, pp.1135-1143, 2015."},{"key":"5","unstructured":"[5] S. Han, H. Mao, and W.J. Dally, \u201cDeep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding,\u201d International Conference on Computer Representations, 2016."},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] X. Yu, T. Liu X. Wang, and D. Tao, \u201cOn compressing deep models by low rank and sparse decomposition,\u201d 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.67-76, 2017. 10.1109\/cvpr.2017.15","DOI":"10.1109\/CVPR.2017.15"},{"key":"7","unstructured":"[7] E.L. Denton, W. Zaremba, J. Bruna, Y. LeCun, and R. Fergus, \u201cExploiting linear structure within convolutional networks for efficient evaluation,\u201d Neural Information Processing Systems, pp.1269-1277, 2014."},{"key":"8","unstructured":"[8] M. Courbariaux, Y. 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Adam, \u201cMobileNets: Efficient convolutional neural networks for mobile vision applications,\u201d Computer Vision and Pattern Recognition, 2017."},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] X. Zhang, X. Zhou, M. Lin, and J. Sun, \u201cShuffleNet: An extremely efficient convolutional neural network for mobile devices,\u201d 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.6848-6856, 2018. 10.1109\/cvpr.2018.00716","DOI":"10.1109\/CVPR.2018.00716"},{"key":"13","unstructured":"[13] B. Baker, O. Gupta, N. Naik, and R. Raskar, \u201c Designing neural network architectures using reinforcement learning,\u201d International Conference on Computer Representations, 2017."},{"key":"14","unstructured":"[14] H. Li, A. Kadav, I. Durdanovic, H. Samet, and H.P. Graf, \u201cPruning filters for efficient convnets,\u201d arXiv preprint arXiv:1608.08710, 2016."},{"key":"15","unstructured":"[15] W. Wen, C. Wu, Y. Wang, Y. Chen, and H. 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