1. Wang, B., et al.: SparG: a sparse GEMM accelerator for deep learning applications. In: Meng, W., Lu, R., Min, G., Vaidya, J. (eds.) Algorithms and Architectures for Parallel Processing: 22nd International Conference, ICA3PP 2022, Copenhagen, Denmark, 10–12 October 2022, Proceedings, pp. 529–547. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-22677-9_28
2. Aktas, K., Ignjatovic, V., Ilic, D., Marjanovic, M., Anbarjafari, G.: Deep convolutional neural networks for detection of abnormalities in chest X-rays trained on the very large dataset. Signal Image Video Process. 17(4), 1035–1041 (2023). https://doi.org/10.1007/s11760-022-02309-w
3. Atakishiyev, S., Salameh, M., Yao, H., Goebel, R.: Explainable artificial intelligence for autonomous driving: a comprehensive overview and field guide for future research directions. CoRR abs/2112.11561 (2021). https://arxiv.org/abs/2112.11561
4. LeCun, Y., Denker, J.S., Solla, S.A.: Optimal brain damage. In: Touretzky, D.S. (ed.) Advances in Neural Information Processing Systems 2, [NIPS Conference, Denver, Colorado, USA, 27–30 November 1989], pp. 598–605. Morgan Kaufmann (1989). http://papers.nips.cc/paper/250-optimal-brain-damage
5. Denil, M., Shakibi, B., Dinh, L., Ranzato, M., de Freitas, N.: Predicting parameters in deep learning. In: Burges, C.J.C., Bottou, L., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a Meeting Held 5–8 December 2013, Lake Tahoe, Nevada, United States, pp. 2148–2156 (2013). https://proceedings.neurips.cc/paper/2013/hash/7fec306d1e665bc9c748b5d2b99a6e97-Abstract.html