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The variational autoencoder (VAE; Kingma, Welling (2014)) is a recently proposed generative model pairing a top-down generative network with a bottom-up recognition network which approximates posterior inference.
Information processing in dynamical systems: foundations of harmony theory
Smolensky, P · 1986
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Connectionist learning of belief networks
Neal, R. M · 1992
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J · 1992
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The Helmholtz machine
Dayan, P., Hinton, G. E., Neal, R. M., and Zemel, R. S · 1995
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., and Teh, Y · 2006
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Studies in lower bounding probability of evidence using the Markov inequality
Gogate, V., Bidyuk, B., and Dechter, R · 2007
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On the quantitative analysis of deep belief networks
Salakhutdinov, R. and Murray, I · 2008
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Evaluating probabilities under high-dimensional latent variable models
Murray, I. and Salakhutdinov, R · 2009
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Deep Boltzmann machines
Salakhutdinov, R. and E., Hinton G · 2009
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y · 2010
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Efficient learning of deep Boltzmann machines
Salakhutdinov, R. and Larochelle, H · 2010
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The neural autoregressive distribution estimator
Larochelle, H., Murray I · 2011
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One-shot learning by inverting a compositional causal process
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B · 2013
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Learning stochastic feedforward neural networks
Tang, Y. and Salakhutdinov, R · 2013
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Deep autoregressive networks
Gregor, K., Danihelka, I., Mnih, A., Blundell, C., and Wierstra, D · 2014
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Reweighted wake-sleep
Bornschein, J. and Bengio, Y · 2015
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Accurate and conservative estimates of MRF log-likelihood using reverse annealing
Burda, Y., Grosse, R. B., and Salakhutdinov, R · 2015
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Training generative neural networks via maximum mean discrepancy optimization
Dziugaite, K. G., Roy, D. M., and Ghahramani, Z · 2015
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DRAW: A recurrent neural network for image generation
Gregor, K., Danihelka, I., Graves, A., Rezende, D. J., and Wierstra, D · 2015
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Scaling up natural gradient by sparsely factorizing the inverse fisher matrix
Grosse, R. and Salakhudinov, R · 2015
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J. L · 2015
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Auto-Encoding Variational Bayes
Kingma, D. P. and Welling, M · 2014
Cited alongside, same era.
Semi-supervised learning with deep generative models
Kingma, D. P., Mohamed, S., Rezende, D. J., and Welling, M · 2014
Cited alongside, same era.
Neural variational inference and learning in belief networks
Mnih, A. and Gregor, K · 2014
Cited alongside, same era.
Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
Cited alongside, same era.
Multiple object recognition with visual attention
Ba, J. L., Mnih, V., and Kavukcuoglu, K · 2015
Cited alongside, same era.
Kulkarni, T. D., Whitney, W., Kohli, P., and Tenenbaum, J. B · 2015
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Generative moment matching networks
Li, Y., Swersky, K., and Zemel, R · 2015
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Variational inference with normalizing flows
Rezende, D. J. and Mohamed, S · 2015
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Markov chain Monte Carlo and variational inference: bridging the gap
Salimans, T., Kingma, D. P., and Welling, M · 2015
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