1. [1] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770-778, 2016. 10.1109/CVPR.2016.90
2. [2] F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1800-1807, July 2017. 10.1109/CVPR.2017.195
3. [3] A.G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “MobileNets: Efficient convolutional neural networks for mobile vision applications,” arXiv:1704.04861, 2017. 10.48550/arXiv.1704.04861
4. [4] A. Howard, M. Sandler, B. Chen, W. Wang, L.C. Chen, M. Tan, G. Chu, V. Vasudevan, Y. Zhu, R. Pang, H. Adam, and Q.V. Le, “Searching for MobileNetV3,” Proc. International Conference on Computer Vision (ICCV'19), pp.1314-1324, Oct. 2019. 10.1109/ICCV.2019.00140
5. [5] R.T.Q. Chen, Y. Rubanova, J. Bettencourt, and D. Duvenaud, “Neural ordinary differential equations,” Proc. Annual Conference on Neural Information Processing Systems (NeuroIPS'18), pp.6572-6583, Dec. 2018. 10.48550/arXiv.1806.07366