[7] T. Chen, L. Liang, T. DING, Z. Zhu, and I. Zharkov. OTOv2: Automatic, generic, user-friendly. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=7ynoX1ojPMT.
[8] Y. Cheng, D. Wang, P. Zhou, and T. Zhang. Model compression and acceleration for deep neural networks: The principles, progress, and challenges. IEEE Signal Processing Magazine, 35(1):126-136, 2018.
[9] P. de Jorge, A. Sanyal, H. Behl, P. Torr, G. Rogez, and P. K. Dokania. Progressive skeletonization: Trimming more fat from a network at initialization. In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id=9GsFOUyUPi.
[10] L. Deng, G. Li, S. Han, L. Shi, and Y. Xie. Model compression and hardware acceleration for neural networks: A comprehensive survey. Proceedings of the IEEE, 108(4):485-532, 2020.
[11] S. Diamond and S. Boyd. CVXPY: A Python-embedded modeling language for convex optimization. Journal of Machine Learning Research, 17(83):1-5, 2016.
[12] X. Dong, S. Chen, and S. Pan. Learning to prune deep neural networks via layer-wise optimal brain surgeon. Advances in Neural Information Processing Systems, 30, 2017.
[13] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id=YicbFdNTTy.
[14] U. Evci, T. Gale, J. Menick, P. S. Castro, and E. Olsen. Rigging the lottery: Making all tickets winners. In International Conference on Machine Learning, pages 2943-2952. PMLR, 2020.
[15] J. Frankle and M. Carbin. The lottery ticket hypothesis: Finding sparse, trainable neural networks. arXiv preprint arXiv:1803.03635, 2018.
[16] J. Frankle, G. K. Dziugaite, D. Roy, and M. Carbin. Linear mode connectivity and the lottery ticket hypothesis. In International Conference on Machine Learning, pages 3259-3269. PMLR, 2020.
[17] J. Frankle, G. K. Dziugaite, D. Roy, and M. Carbin. Pruning neural networks at initialization: Why are we missing the mark? In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id=Ig-VyQc-MLK.
[18] T. Gale, E.elsen, and S. Hooker. The state of sparsity in deep neural networks. arXiv preprint arXiv:1902.09574, 2019.
[19] T. Gebhart, U. Saxena, and P. Schrater. A unified paths perspective for pruning at initialization. ArXiv, abs/2101.10552, 2021.
[20] S. Han, J. Pool, J. Tran, and W. Dally. Learning both weights and connections for efficient neural network. Advances in neural information processing systems, 28, 2015.
[21] B. Hassibi, D. G. Stork, and G. J. Wolff. Optimal brain surgeon and general network pruning. In IEEE international conference on neural networks, pages 293-299. IEEE, 1993.
[22] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.
[23] G. Hinton, O. Vinyals, J. Dean, et al. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, 2(7), 2015.
[24] T. Hoefler, D. Alistarh, T. Ben-Nun, N. Dryden, and A. Peste. Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks. J. Mach. Learn. Res., 22(1), jan 2021.
[25] Y. LeCun, J. Denker, and S. Solla. Optimal brain damage. Advances in neural information processing systems, 2, 1989.