Deep learning has been shown as a successful machine learning method for a variety of tasks, and its popularity results in numerous open-source deep learning software tools. Training a deep network is usually a very time-consuming process. To address the computational c... View more
L. Deng, “Three classes of deep learning architectures and their applications: a tutorial survey,” APSIPA transactions on signal and information processing, 2012.
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” in Proceedings of the 22nd ACM international conference on Multimedia, 2014, pp. 675-678.
D. Yu, A. Eversole, M. Seltzer, K. Yao, Z. Huang, B. Guenter, O. Kuchaiev, Y. Zhang, F. Seide, H. Wang et al., “An introduction to computational networks and the computational network toolkit,” Technical report, Tech. Rep. MSR, Microsoft Research, 2014, 2014.
Corrado, A. Davis, J. Dean, M. Devin et al., “Tensorflow: Largescale machine learning on heterogeneous systems, 2015,” Software available from tensorflow. org, vol. 1, 2015.
R. Collobert, K. Kavukcuoglu, and C. Farabet, “Torch7: A matlablike environment for machine learning,” in BigLearn, NIPS Workshop, no. EPFL-CONF-192376, 2011.
T. T. D. Team, R. Al-Rfou, G. Alain, A. Almahairi, C. Angermueller, D. Bahdanau, N. Ballas, F. Bastien, J. Bayer, A. Belikov et al., “Theano: A python framework for fast computation of mathematical expressions,” arXiv preprint arXiv:1605.02688, 2016.
T. Chen, M. Li, Y. Li, M. Lin, N. Wang, M. Wang, T. Xiao, B. Xu, C. Zhang, and Z. Zhang, “Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems,” arXiv preprint arXiv:1512.01274, 2015.