Publisher
Springer Science and Business Media LLC
Subject
Hardware and Architecture,Modelling and Simulation,Information Systems,Signal Processing,Theoretical Computer Science,Control and Systems Engineering
Reference60 articles.
1. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.
2. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ..., Rabinovich, A. (2015, June). Going deeper with convolutions. Cvpr.
3. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
4. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
5. Denil, M., Shakibi, B., Dinh, L., & De Freitas, N. (2013). Predicting parameters in deep learning. In Advances in neural information processing systems (pp. 2148-2156).
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献