[48] Jian-Hao Luo, Jianxin Wu, and Weiyao Lin. Thinet: A filter level pruning method for deep neural network compression. In IEEE International Conference on Computer Vision (ICCV), pages 5058-5066, 2017.
[49] Fanxu Meng, Hao Cheng, Ke Li, Huixiang Luo, Xiaowei Guo, Guangming Lu, and Xing Sun. Pruning filter in filter. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 17629-17640. Curran Associates, Inc., 2020.
[50] Asit Mishra, Jorge Albericio Latorre, Jeff Pool, Darko Stosic, Dusan Stosic, Ganesh Venkatesh, Chong Yu, and Paulius Micikevicius. Accelerating sparse deep neural networks. arXiv preprint arXiv:2104.08378, 2021.
[51] Pavlo Molchanov, Arun Mallya, Stephen Tyree, Iuri Frosio, and Jan Kautz. Importance estimation for neural network pruning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 11264-11272, 2019.
[52] Xuefei Ning, Tianchen Zhao, Wenshuo Li, Peng Lei, Yu Wang, and Huazhong Yang. Dsa: More efficient budgeted pruning via differentiable sparsity allocation. In European Conference on Computer Vision (ECCV), pages 592-607. Springer, 2020.
[53] Jeff Pool and Chong Yu. Channel permutations for n: M sparsity. Advances in Neural Information Processing Systems (NeurIPS), 34, 2021.
[54] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems, pages 91-99, 2015.
[55] Olivier Giroux et al. Ronny Krashinsky. Nvidia ampere sparse tensor core. https://developer.nvidia.com/blog/nvidia-ampere-architecture-in-depth/, 2020.
[56] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations (ICLR), 2015.
[57] Xiu Su, Shan You, Tao Huang, Fei Wang, Chen Qian, Changshui Zhang, and Chang Xu. Locally free weight sharing for network width search. Proceedings of International Conference on Learning Representations, 2021.
[58] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1-9, 2015.
[59] Yehui Tang, Yunhe Wang, Yixing Xu, Dacheng Tao, Chunjing Xu, Chao Xu, and Chang Xu. Scop: Scientific control for reliable neural network pruning. Proceedings of Advances in Neural Information Processing Systems, 2020.
[60] Carl Yang, Aydin Buluc, and John D Owens. Design principles for sparse matrix multiplication on thegpu. In Euro-Par 2018: Parallel Processing: 24th International Conference on Parallel and Distributed Computing, Turin, Italy, August 27-31, 2018, Proceedings, pages 672-687. Springer, 2018.
[61] Tien-Ju Yang, Andrew Howard, Bo Chen, Xiao Zhang, Alec Go, Mark Sandler, Vivienne Sze, and Hartwig Adam. Netadapt: Platform-aware neural network adaptation for mobile applications. In Proceedings of the European Conference on Computer Vision (ECCV), pages 285-300, 2018.
[62] Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam Klivans, and Qiang Liu. Good subnetworks provably exist: Pruning via greedy forward selection. Proceedings of International Conference on Machine Learning, pages 10820-10830, 2020.