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Training deep directed graphical models with many hidden variables and performing inference remains a major challenge.
The Helmholtz machine
Dayan, P., Hinton, G. E., Neal, R. M., and Zemel, R. S. (1995) · 1995
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The wake-sleep algorithm for unsupervised neural networks
Hinton, G. E., Dayan, P., Frey, B. J., and Neal, R. M. (1995) · 1995
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Varieties of helmholtz machine
Dayan, P. and Hinton, G. E. (1996) · 1996
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Mean field theory for sigmoid belief networks
Saul, L. K., Jaakkola, T., and Jordan, M. I. (1996) · 1996
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Graphical models for machine learning and digital communication
Frey, B. J. (1998) · 1998
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Modeling high-dimensional discrete data with multi-layer neural networks
Bengio, Y. and Bengio, S. (2000) · 2000
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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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On the quantitative analysis of deep belief networks
Salakhutdinov, R. and Murray, I. (2008) · 2008
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Learning deep architectures for AI
Bengio, Y. (2009) · 2009
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Evaluating probabilities under high-dimensional latent variable models
Murray, I. and Salakhutdinov, R. (2009) · 2009
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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
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Binarized mnist dataset
Larochelle, H. (2011) · 2011
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The Neural Autoregressive Distribution Estimator
Larochelle, H. and Murray, I. (2011) · 2011
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Theano: new features and speed improvements
Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I. J., Bergeron, A., Bouchard, N., and Bengio, Y. (2012) · 2012
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Enhanced gradient for training restricted boltzmann machines
Cho, K., Raiko, T., and Ilin, A. (2013) · 2013
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Learning stochastic feedforward neural networks
Tang, Y. and Salakhutdinov, R. (2013) · 2013
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Deep autoregressive networks
Gregor, K., Danihelka, I., Mnih, A., Blundell, C., and Wierstra, D. (2014) · 2014
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2014) · 2014
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Neural variational inference and learning in belief networks
Mnih, A. and Gregor, K. (2014) · 2014
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A deep and tractable density estimator
Murray, B. U. I. and Larochelle, H. (2014) · 2014
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Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription
Boulanger-Lewandowski, N., Bengio, Y., and Vincent, P. (2012) · 2012
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Better mixing via deep representations
Bengio, Y., Mesnil, G., Dauphin, Y., and Rifai, S. (2013) · 2013
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Rezende, D. J., Mohamed, S., and Wierstra, D. (2014) · 2014
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