[1] Syed Asad Alam, Andrew Anderson, Barbara Barabasz, and David Gregg. Winograd convolution for deep neural networks: Efficient point selection. ACM Transactions on Embedded Computing Systems, 21(6):1-28, 2022.
[2] Shun-ichi Amari. Backpropagation and stochastic gradient descent method. Neurocomputing, 5(4-5):185-196, 1993.
[3] Barbara Barabasz, Andrew Anderson, Kirk M Soodhalter, and David Gregg. Error analysis and improving the accuracy of winograd convolution for deep neural networks. ACM Transactions on Mathematical Software (TOMS), 46(4):1-33, 2020.
[4] Yoshua Bengio, Nicholas Léonard, and Aaron Courville. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432, 2013.
[5] Kumar Chellapilla, Sidd Puri, and Patrice Simard. High performance convolutional neural networks for document processing. In Tenth international workshop on frontiers in handwriting recognition. Suvisoft, 2006.
[6] Minsu Cho, Zahra Ghodsi, Brandon Reagen, Siddharth Garg, and Chinmay Hegde. Sphynx: A deep neural network design for private inference. IEEE Security & Privacy, 20(5):22-34, 2022.
[7] Minsu Cho, Ameya Joshi, Brandon Reagen, Siddharth Garg, and Chinmay Hegde. Selective network linearization for efficient private inference. In International Conference on Machine Learning, pages 3947-3961. PMLR, 2022.
[8] Daniel Demmler, Thomas Schneider, and Michael Zohner. Aby-a framework for efficient mixed-protocol secure two-party computation. In NDSS, 2015.
[9] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. IEEE, 2009.
[10] Xiaohan Ding, Honghao Chen, Xiangyu Zhang, Jungong Han, and Guiguang Ding. Repmplnet: Hierarchical vision mlp with re-parameterized locality. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 578-587, 2022.
[11] Xiaohan Ding, Yuchen Guo, Guiguang Ding, and Jungong Han. Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1911-1920, 2019.
[12] Xiaohan Ding, Tianxiang Hao, Jianchao Tan, Ji Liu, Jungong Han, Yuchen Guo, and Guiguang Ding. Resrep: Lossless cnn pruning via decoupling remembering and forgetting. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4510-4520, 2021.
[13] Xiaohan Ding, Xiangyu Zhang, Jungong Han, and Guiguang Ding. Diverse branch block: Building a convolution as an inception-like unit. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10886-10895, 2021.
[14] Xiaohan Ding, Xiangyu Zhang, Ningning Ma, Jungong Han, Guiguang Ding, and Jian Sun. Repvgg: Making vgg-style convnets great again. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 13733-13742, 2021.
[15] Javier Fernandez-Marques, Paul Whatmough, Andrew Mundy, and Matthew Mattina. Searching for winograd-aware quantized networks. Proceedings of Machine Learning and Systems, 2:14-29, 2020.