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What do auto-encoders learn about the underlying data generating distribution? Recent work suggests that some auto-encoder variants do a good job of capturing the local manifold structure of data.
Sparse coding with an overcomplete basis set: a strategy employed by V1?
B. A. Olshausen and D. J. Field · 1997
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Introduction to the Calculus of Variations
B. Dacorogna · 2004
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Algorithms for manifold learning
Lawrence Cayton · 2005
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Estimation of non-normalized statistical models using score matching
Aapo Hyvärinen · 2005
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A fast learning algorithm for deep belief nets
Geoffrey E. Hinton, Simon Osindero, and Yee Whye Teh · 2006
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Greedy layer-wise training of deep networks
Yoshua Bengio, Pascal Lamblin, Dan Popovici, and Hugo Larochelle · 2007
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Some extensions of score matching
Aapo Hyvärinen · 2007
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Efficient learning of sparse representations with an energy-based model
Marc’Aurelio Ranzato, Christopher Poultney, Sumit Chopra, and Yann LeCun · 2007
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Natural image denoising with convolutional networks
Viren Jain and Sebastian H. Seung · 2008
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Sparse feature learning for deep belief networks
Marc’Aurelio Ranzato, Y-Lan Boureau, and Yann LeCun · 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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Learning deep architectures for AI
Yoshua Bengio · 2009
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Learning invariant features through topographic filter maps
Koray Kavukcuoglu, Marc’Aurelio Ranzato, Rob Fergus, and Yann LeCun · 2009
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Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y. Ng · 2009
Sample complexity of testing the manifold hypothesis
Hariharan Narayanan and Sanjoy Mitter · 2010
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On the expressive power of deep architectures
Yoshua Bengio and Olivier Delalleau · 2011
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Structured sparse coding via lateral inhibition
Karol Gregor, Arthur Szlam, and Yann LeCun · 2011
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The manifold tangent classifier
Salah Rifai, Yann Dauphin, Pascal Vincent, Yoshua Bengio, and Xavier Muller · 2011
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Contractive auto-encoders: Explicit invariance during feature extraction
Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, and Yoshua Bengio · 2011
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On autoencoders and score matching for energy based models
Kevin Swersky, Marc’Aurelio Ranzato, David Buchman, Benjamin Marlin, and Nando de Freitas · 2011
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Deep Boltzmann machines
R. Salakhutdinov and G.E. Hinton · 2009
Cited alongside, same era.
Regularized estimation of image statistics by score matching
Diederik Kingma and Yann LeCun · 2010
Cited alongside, same era.
Implicit density estimation by local moment matching to sample from auto-encoders
Yoshua Bengio, Guillaume Alain, and Salah Rifai
Cited in the paper.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent
Cited in the paper.
Generalized denoising auto-encoders as generative models
Yoshua Bengio, Yao Li, Guillaume Alain, and Pascal Vincent
Cited in the paper.
Better mixing via deep representations
Yoshua Bengio, Grégoire Mesnil, Yann Dauphin, and Salah Rifai
Cited in the paper.
A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
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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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