On the quantitative analysis of deep belief networks
暂无分享,去创建一个
[1] Radford M. Neal. Annealed importance sampling , 1998, Stat. Comput..
[2] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[3] Radford M. Neal. Estimating Ratios of Normalizing Constants Using Linked Importance Sampling , 2005, math/0511216.
[4] William T. Freeman,et al. Constructing free-energy approximations and generalized belief propagation algorithms , 2005, IEEE Transactions on Information Theory.
[5] Miguel Á. Carreira-Perpiñán,et al. On Contrastive Divergence Learning , 2005, AISTATS.
[6] Martin J. Wainwright,et al. A new class of upper bounds on the log partition function , 2002, IEEE Transactions on Information Theory.
[7] Peter V. Gehler,et al. The rate adapting poisson model for information retrieval and object recognition , 2006, ICML.
[8] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[9] Geoffrey E. Hinton,et al. Modeling Human Motion Using Binary Latent Variables , 2006, NIPS.
[10] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[11] Geoffrey E. Hinton,et al. Restricted Boltzmann machines for collaborative filtering , 2007, ICML '07.
[12] Jason Weston,et al. Large-scale kernel machines , 2007 .
[13] Geoffrey E. Hinton,et al. Modeling image patches with a directed hierarchy of Markov random fields , 2007, NIPS.
[14] Nicolas Le Roux,et al. Representational Power of Restricted Boltzmann Machines and Deep Belief Networks , 2008, Neural Computation.