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Recent work has shown how denoising and contractive autoencoders implicitly capture the structure of the data-generating density, in the case where the corruption noise is Gaussian, the reconstruction error is the squared error, and the data is continuous-valued.
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Hinton, G. E. (1999) · 1999
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Dependency networks for inference, collaborative filtering, and data visualization
Heckerman, D., Chickering, D. M., Meek, C., Rounthwaite, R., and Kadie, C. (2000) · 2000
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Estimation of non-normalized statistical models using score matching
Hyvärinen, A. (2005) · 2005
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A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., and Teh, Y. (2006) · 2006
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Sparse feature learning for deep belief networks
Ranzato, M., Boureau, Y.-L., and LeCun, Y. (2008) · 2008
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Theano: a CPU and GPU math expression compiler
Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., and Bengio, Y. (2010) · 2010
Cited alongside, same era.
Regularized estimation of image statistics by score matching
Kingma, D. and LeCun, Y. (2010) · 2010
Cited alongside, same era.
Contractive auto-encoders: Explicit invariance during feature extraction
Rifai, S., Vincent, P., Muller, X., Glorot, X., and Bengio, Y. (2011) · 2011
Cited alongside, same era.
On autoencoders and score matching for energy based models
Swersky, K., Ranzato, M., Buchman, D., Marlin, B., and de Freitas, N. (2011) · 2011
Cited alongside, same era.
A connection between score matching and denoising autoencoders
Vincent, P. (2011) · 2011
Cited alongside, same era.
Non-local manifold Parzen windows
Bengio, Y., Larochelle, H., and Vincent, P. (2006a)
Cited in the paper.
Nonlocal estimation of manifold structure
Bengio, Y., Monperrus, M., and Larochelle, H. (2006b)
Cited in the paper.
Deep generative stochastic networks trainable by backprop
Bengio, Y., Thibodeau-Laufer, E., and Yosinski, J. (2013a)
Cited in the paper.
Unsupervised feature learning and deep learning: A review and new perspectives
Bengio, Y., Courville, A., and Vincent, P. (2013b)
Cited in the paper.
A generative process for sampling contractive auto-encoders
Rifai, S., Bengio, Y., Dauphin, Y., and Vincent, P. (2012) · 2012
Later among the works it cites.
What regularized auto-encoders learn from the data generating distribution
Alain, G. and Bengio, Y. (2013) · 2013
Closest in time.
Bounding the test log-likelihood of generative models
Bengio, Y. and Yao, L. (2013) · 2013
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Enhanced gradient for training restricted boltzmann machines
Cho, K., Raiko, T., and Ilin, A. (2013) · 2013
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