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Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, via Masked Autoregressive Flows (MAF), and to accelerate state-of-the-art WaveNet-based speech synthesis to 20x faster than real-time, via Inverse Autoregressive Flows (IAF).
Approximation by superpositions of a sigmoidal function
Cybenko, George · 1989
Earlier work this paper cites.
Acceleration of stochastic approximation by averaging
Polyak, Boris T and Juditsky, Anatoli B · 1992
Earlier work this paper cites.
Nonlinear independent component analysis: Existence and uniqueness results
Hyvärinen, Aapo and Pajunen, Petteri · 1999
Earlier work this paper cites.
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
Martin, David, Fowlkes, Charless, Tal, Doron, and Malik, Jitendra · 2001
Earlier work this paper cites.
The neural autoregressive distribution estimator
Larochelle, Hugo and Murray, Iain · 2011
Earlier work this paper cites.
Two problems with variational expectation maximisation for time-series models
Turner, Richard E and Sahani, Maneesh · 2011
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, Diederik P and Welling, Max · 2013
Earlier work this paper cites.
UCI machine learning repository, 2013
Lichman, M · 2013
Earlier work this paper cites.
High-dimensional probability estimation with deep density models
Rippel, Oren and Adams, Ryan Prescott · 2013
Earlier work this paper cites.
Rnade: The real-valued neural autoregressive density-estimator
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Earlier work this paper cites.
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Bahdanau, Dzmitry, Cho, Kyunghyun, and Bengio, Yoshua · 2014
Earlier work this paper cites.
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Dinh, Laurent, Krueger, David, and Bengio, Yoshua · 2014
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, Ian, Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron, and Bengio, Yoshua · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Rezende, Danilo Jimenez, Mohamed, Shakir, and Wierstra, Daan · 2014
Earlier work this paper cites.
Fast and accurate deep network learning by exponential linear units (elus)
Clevert, Djork-Arné, Unterthiner, Thomas, and Hochreiter, Sepp · 2015
Earlier work this paper cites.
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van den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., and Kavukcuoglu, K · 2016
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Learnable explicit density for continuous latent space and variational inference
Huang, Chin-Wei, Touati, Ahmed, Dinh, Laurent, Drozdzal, Michal, Havaei, Mohammad, Charlin, Laurent, and Courville, Aaron · 2017
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Krueger, David, Huang, Chin-Wei, Islam, Riashat, Turner, Ryan, Lacoste, Alexandre, and Courville, Aaron · 2017
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