[50] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
[51] Samuel L Smith and Quoc V Le. A bayesian perspective on generalization and stochastic gradient descent. arXiv preprint arXiv:1710.06451, 2017.
[52] Ghada Sokar, Decebal Constantin Mocanu, and Mykola Pechenizkiy. SpaceNet: Make Free Space For Continual Learning. Neurocomputing, 439:1-11, 2021. URL: https://arxiv.org/abs/2007.07617.
[53] Ghada Sokar, Elena Mocanu, Decebal Constantin Mocanu, Mykola Pechenizkiy, and Peter Stone. Dynamic Sparse Training for Deep Reinforcement Learning. arXiv preprint arXiv:2106.04217, 2021. URL: https://arxiv.org/abs/2106.04217.
[54] Kartik Sreenivasan, Jy-yong Sohn, Liu Yang, Matthew Grinde, Alliot Nagle, Hongyi Wang, Kangwook Lee, and Dimitris Papailiopoulos. Rare Gems: Finding Lottery Tickets at Initialization. arXiv preprint arXiv:2202.12002, 2022. URL: https://arxiv.org/abs/2202.12002.
[55] Mingjie Sun, Zhuang Liu, Anna Bair, and J Zico Kolter. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695, 2023.
[56] Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. Commonsenseqa: A question answering challenge targeting commonsense knowledge. arXiv preprint arXiv:1811.00937, 2018.
[57] Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105-6114. PMLR, 2019.
[58] Yiqin Tan, Pihe Hu, Ling Pan, and Longbo Huang. RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch. arXiv preprint arXiv:2205.15043, 2022. URL: https://arxiv.org/abs/2205.15043.
[59] Hidenori Tanaka, Daniel Kunin, Daniel L Yamins, and Surya Ganguli. Pruning neural networks without any data by iteratively conserving synaptic flow. Advances in neural information processing systems, 33:6377-6389, 2020.
[60] Chaoqi Wang, Guodong Zhang, and Roger Grosse. Picking Winning Tickets Before Training by Preserving Gradient Flow. International Conference on Learning Representations, 2020. URL: https://arxiv.org/abs/2002.07376.
[61] Mitchell Wortsman, Ali Farhadi, and Mohammad Rastegari. Discovering Neural Wiring. Advances in Neural Information Processing Systems, 32, 2019. URL: https://arxiv.org/abs/1906.00586.
[62] Huikai Wu, Junge Zhang, and Kaiqi Huang. SparseMask: Differentiable Connectivity Learning for Dense Image Prediction. In Proceedings of the IEEE International Conference on Computer Vision, pages 6768-6777, 2019. URL: https://arxiv.org/abs/1904.07642.
[63] Han Xiao, Kashif Rasul, and Roland Vollgraf. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747, 2017.
[64] Geng Yuan, Xiaolong Ma, Wei Niu, Zhengang Li, Zhenglun Kong, Ning Liu, Yifan Gong, Zheng Zhan, Chaoyang He, Qing Jin, et al. MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge. Advances in Neural Information Processing Systems, 34:20838-20850, 2021. URL: https://arxiv.org/abs/2110.14032.
[65] Aojun Zhou, Yukun Ma, Junnan Zhu, Jianbo Liu, Zhijie Zhang, Kun Yuan, Wenxiu Sun, and Hongsheng Li. Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch. International Conference on Learning Representations, 2020. URL: https://arxiv.org/abs/2102.04010.
[66] Hattie Zhou, Janice Lan, Rosanne Liu, and Jason Yosinski. Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask. Advances in Neural Information Processing Systems, 32, 2019. URL: https://arxiv.org/abs/1905.01067.