1BENGIO Y, DELALLEAU O. On the expressive power of deep archi- tectures[ C ]//Proc of the 14th International Conference on Discovery Science. Berlin : Springer-Verlag, 2011 : 18 - 36.
2BENGIO Y. Leaming deep architectures for AI[ J]. Foundations and Trends in Machine Learning ,2009,2 ( 1 ) : 1-127.
3HINTON G,OSINDERO S,TEH Y. A fast learning algorithm for deep belief nets [ J ]. Neural Computation ,2006,18 (7) : 1527-1554.
4BENGIO Y, LAMBLIN P, POPOVICI D, et al. Greedy layer-wise training of deep networks [ C ]//Proc of the 12th Annual Conference on Neural Information Processing System. 2006:153-160.
5LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning ap- plied to document recognition[ J]. Proceedings of the iEEE, 1998, 86( 11 ) :2278-2324.
6VINCENT P, LAROCHELLE H, BENGIO Y, et al. Extracting and composing robust features with denoising autoencoders[ C ]//Proc of the 25th International Conference on Machine Learning. New York: ACM Press ,2008 : 1096-1103.
7VINCENT P, LAROCHELLE H, LAJOIE I, et aL Stacked denoising autoencoders:learning useftd representations in a deep network with a local denoising criterion [ J ]. Journal of Machine Learning Re- search ,2010,11 ( 12 ) :3371-3408.
8YU Dong, DENG Li. Deep convex net: a scalable architecture for speech pattern classification [ C]//Proc of the 12th Annual Confe-rence of International Speech Comunication Association. 2011 : 2285- 2288.
9POON H, DOMINGOS P. Sum-product networks:a new deep architec- ture[ C ]//Proc of IEEE Intemational Conference on Computer Vi- sion. 2011:689-690.
10BENGIO Y,LECUN Y. Scaling learning algorithms towards AI[ M]// BOTTOU L,CHAPELLE O, DeCOSTE D,et al. Large-Scale Kernel Machines. Cambridge: MIT Press ,2007:321-358.
