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Probabilistic generative models can be used for compression, denoising, inpainting, texture synthesis, semi-supervised learning, unsupervised feature learning, and other tasks.
A parametric texture model based on joint statistics of complex wavelet coefficients
Portilla, J. and Simoncelli, E. P · 2000
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A mathematical theory of communication
Shannon, C. E · 2001
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Training Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E · 2002
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Estimation of non-normalized statistical models using score matching
Hyvärinen, A · 2005
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Pattern Recognition and Machine Learning
Bishop, C. M · 2006
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Principled hybrids of generative and discriminative models
Lasserre, J. A., Bishop, C. M., and Minka, T. P · 2006
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A kernel method for the two-sample-problem
Gretton, A., Borgwardt, K. M., Rasch, M., Schölkopf, B., and Smola, A. J · 2007
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Scene completion using millions of photographs
Hays, J. and Efros, A. A · 2007
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Unlearning for better mixing
Breuleux, O., Bengio, Y., and Vincent, P · 2009
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Natural Image Statistics: A Probabilistic Approach to Early Computational Vision
Hyvärinen, A., Hurri, J., and Hoyer, P. O · 2009
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Mean squared error: Love it or leave it?
Wang, Z. and Bovik, A. C · 2009
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, M. and Hyvärinen, A · 2010
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Approximate inference for the loss-calibrated Bayesian
Lacoste-Julien, S., Huszar, F., and Ghahramani, Z · 2011
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Minimum Probability Flow Learning
Sohl-Dickstein, J., Battaglino, P., and DeWeese, M. R · 2011
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Bayesian Learning via Stochastic Gradient Langevin Dynamics
Welling, M. and Teh, Y. W · 2011
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How sensitive is the human visual system to the local statistics of natural images?
Gerhard, H. E., Wichmann, F. A., and Bethge, M · 2013
Cited alongside, same era.
Variational Generative Stochastic Networks with Collaborative Shaping
Bachman, P. and Precup, D · 2015
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Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
Denton, E., Chintala, S., Szlam, A., and Fergus, R · 2015
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Learning to Generate Chairs with Convolutional Neural Networks
Dosovitskiy, A., Springenberg, J. T., and Brox, T · 2015
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Training generative neural networks via maximum mean discrepancy optimization, 2015
Dziugaite, G. K., Roy, D. M., and Ghahramani, Z · 2015
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Gatys, L. A., Ecker, A. S., and Bethge, M · 2015
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RNADE: The real-valued neural autoregressive density-estimator
Uria, B., Murray, I., and Larochelle, H · 2013
Cited alongside, same era.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Cited alongside, same era.
Semi-supervised learning with deep generative models
Kingma, D. P., Rezende, D. J., Mohamed, S., and Welling, M · 2014
Cited alongside, same era.
Factoring Variations in Natural Images with Deep Gaussian Mixture Models
van den Oord, A. and Schrauwen, B · 2014
Cited alongside, same era.
Better mixing via deep representations
Bengio, Y., Mesnil, G., Dauphin, Y., and Rifai, S
Cited in the paper.
Deep generative stochastic networks trainable by backprop, 2013b
Bengio, Y., Thibodeau-Laufer, E., Alain, G., and Yosinski, J
Cited in the paper.
DRAW: A recurrent neural network for image generation
Gregor, K., Danihelka, I., Graves, A., and Wierstra, D · 2015
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Generative moment matching networks
Li, Y., Swersky, K., and Zemel, R · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E. A., Maheswaranathan, N., and Ganguli, S · 2015
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Generative Image Modeling Using Spatial LSTMs
Theis, L. and Bethge, M · 2015
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Locally-connected transformations for deep GMMs, 2015
van den Oord, A. and Dambre, J · 2015
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