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One of the fundamental problems in machine learning is the estimation of a probability distribution from data.
Information processing in dynamical systems: Foundations of harmony theory
P. Smolensky · 1986
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Learning internal representations from gray-scale images: An example of extensional programming
Garrison W. Cottrell, Paul Munro, and David Zipser · 1987
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Probabilistic reasoning in intelligent systems: networks of plausible inference
J. Pearl · 1988
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Connectionist learning in belief networks
Radford M. Neal · 1992
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Bayesian density estimation and inference using mixtures
Michael D. Escobar and Mike West · 1995
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An information-maximization approach to blind separation and blind deconvolution
A.J. Bell and T.J. Sejnowski · 1995
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Density networks
David J.C. MacKay and Mark N. Gibbs · 1997
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Nonlinear component analysis as a kernel eigenvalue problem
B. Schölkopf, A. Smola, and K.R. Müller · 1998
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The infinite Gaussian mixture model
Carl Edward Rasmussen · 2000
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Nonlinear dimensionality reduction by locally linear embedding
S.T. Roweis and L.K. Saul · 2000
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A global geometric framework for nonlinear dimensionality reduction
J.B. Tenenbaum, V. De Silva, and J.C. Langford · 2000
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Manifold Parzen windows
Pascal Vincent and Yoshua Bengio · 2002
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Probabilistic non-linear principal component analysis with Gaussian process latent variable models
N. Lawrence · 2005
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A fast learning algorithm for deep belief nets
G.E. Hinton, S. Osindero, and Y.W. Teh · 2006
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Visualizing data using t-SNE
L. Van der Maaten and G. Hinton · 2008
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Diffeomorphic dimensionality reduction
Christian Walder and Bernhard Schölkopf · 2008
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Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
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The Gaussian process density sampler
Ryan P. Adams, Iain Murray, and David J. C. MacKay · 2009
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Learning the structure of deep sparse graphical models
Ryan P. Adams, Hanna M. Wallach, and Zoubin Ghahramani · 2010
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Bayesian Gaussian process latent variable model
M. Titsias and N. Lawrence · 2010
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Reducing the dimensionality of data with neural networks
Geoffrey Hinton and Ruslan Salakhutdinov · 2006
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Local distance preservation in the GP-LVM through back constraints
Neil D. Lawrence and Joaquin Quiñonero Candela · 2006
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A generative process for sampling contractive auto-encoders
Salah Rifai, Yoshua Bengio, Yann Dauphin, and Pascal Vincent · 2012
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