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Samet, and H.P. Graf, Pruning filters for efficient convnets, Proc. International Conference on Learning Representations, pp.1-13, 2017."},{"key":"10","unstructured":"[10] B. Liu, M. Wang, H. Foroosh, M. Tappen, and M. Penksy, \u201cSparse convolutional neural networks,\u201d Proc. Computer Vision and Pattern Recognition, pp.806-814, 2015. 10.1109\/cvpr.2015.7298681"},{"key":"11","unstructured":"[11] P. Molchanov, S. Tyree, T. Karras, T. Aila, and J. Kautz, Pruning convolutional neural networks for resource efficient inference, Proc. International Conference on Learning Representations, pp.1-17, 2017."},{"key":"12","unstructured":"[12] A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, Automatic differentiation in pytorch, NIPS-W, 2017."},{"key":"13","unstructured":"[13] K. Simonyan and A. Zisserman, Very deep convoolutional networks for large-scale image recognition, Proc. 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