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Here we propose a novel model family with the objective of learning to disentangle the factors of variation in data.
Spectral classification of phonemes by learning subspaces
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Learning to represent spatial transformations with factored higher-order boltzmann machines
The Toronto face dataset
J. Susskind, A. Anderson, and G. E. Hinton · 2010
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Structured sparsity through convex optimization
F. Bach, R. Jenatton, J. Mairal, and G. Obozinski · 2011
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R. Memisevic and G. E. Hinton · 2010
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Modeling pixel means and covariances using factorized third-order Boltzmann machines
M. Ranzato and G. H. Hinton · 2010
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Generating more realistic images using gated MRF’s
M. Ranzato, V. Mnih, and G. Hinton · 2010
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Transforming auto-encoders
G. Hinton, A. Krizhevsky, and S. Wang · 2011
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On deep generative models with applications to recognition
M. Ranzato, J. Susskind, V. Mnih, and G. Hinton · 2011
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