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We consider high-dimensional distribution estimation through autoregressive networks.
Maximum likelihood from incomplete data via the em algorithm
Arthur P Dempster, Nan M Laird, and Donald B Rubin · 1977
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A tutorial on hidden markov models and selected applications in speech recognition
Lawrence R Rabiner · 1989
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Mixture density networks
Christopher M Bishop · 1994
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Image analysis with partially ordered markov models
Noel Cressie and Jennifer L Davidson · 1998
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Graphical models for machine learning and digital communication
Brendan J Frey · 1998
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Taking on the curse of dimensionality in joint distributions using neural networks
Samy Bengio and Yoshua Bengio · 2000
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Mix-nets: Factored mixtures of gaussians in bayesian networks with mixed continuous and discrete variables
Scott Davies and Andrew Moore · 2000
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Learning montages of transformed latent images as representations of objects that change in appearance
Chris Pal, Brendan J Frey, and Nebojsa Jojic · 2002
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Object class recognition by unsupervised scale-invariant learning
Robert Fergus, Pietro Perona, and Andrew Zisserman · 2003
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Regularized multi–task learning
Theodoros Evgeniou and Massimiliano Pontil · 2004
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Regularization and variable selection via the elastic net
Hui Zou and Trevor Hastie · 2005
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High-dimensional graphs and variable selection with the lasso
Nicolai Meinshausen and Peter Bühlmann · 2006
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Mosaicfaces: a discrete representation for face recognition
Jania Aghajanian and Simon JD Prince · 2008
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Combined top-down/bottom-up segmentation
Eran Borenstein and Shimon Ullman · 2008
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Who killed the directed model?
Justin Domke, Alap Karapurkar, and Yiannis Aloimonos · 2008
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On the quantitative analysis of deep belief networks
Ruslan Salakhutdinov and Iain Murray · 2008
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Inductive principles for restricted boltzmann machine learning
Benjamin M Marlin, Kevin Swersky, Bo Chen, and Nando D Freitas · 2010
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pierre Vincent · 2013
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Rnade: The real-valued neural autoregressive density-estimator
Benigno Uria, Iain Murray, and Hugo Larochelle · 2013
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The shape boltzmann machine: a strong model of object shape
SM Ali Eslami, Nicolas Heess, Christopher KI Williams, and John Winn · 2014
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Deep autoregressive networks
Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, and Daan Wierstra · 2014
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Iterative neural autoregressive distribution estimator nade-k
Tapani Raiko, Yao Li, Kyunghyun Cho, and Yoshua Bengio · 2014
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A deep and tractable density estimator
Benigno Uria, Iain Murray, and Hugo Larochelle · 2014
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