Helmholtz Machines and Wake-Sleep Learning

[1]  Michael I. Jordan Learning in Graphical Models , 1999, NATO ASI Series.

[2]  Geoffrey E. Hinton,et al.  A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.

[3]  Peter Dayan,et al.  Factor Analysis Using Delta-Rule Wake-Sleep Learning , 1997, Neural Computation.

[4]  Geoffrey E. Hinton,et al.  Generative models for discovering sparse distributed representations. , 1997, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[5]  Rajesh P. N. Rao,et al.  Dynamic Model of Visual Recognition Predicts Neural Response Properties in the Visual Cortex , 1997, Neural Computation.

[6]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[7]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[8]  Geoffrey E. Hinton,et al.  The Helmholtz Machine , 1995, Neural Computation.

[9]  Geoffrey E. Hinton,et al.  The "wake-sleep" algorithm for unsupervised neural networks. , 1995, Science.

[10]  Alan S. Willsky,et al.  Likelihood calculation for a class of multiscale stochastic models, with application to texture discrimination , 1995, IEEE Trans. Image Process..

[11]  Radford M. Neal Connectionist Learning of Belief Networks , 1992, Artif. Intell..

[12]  Geoffrey E. Hinton,et al.  Learning and relearning in Boltzmann machines , 1986 .

[13]  Geoffrey E. Hinton Products of experts , 1999 .

[14]  R. Zemel A minimum description length framework for unsupervised learning , 1994 .

[15]  Mitsuo Kawato,et al.  A forward-inverse optics model of reciprocal connections between visual cortical areas , 1993 .