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A new method for learning variational autoencoders (VAEs) is developed, based on Stein variational gradient descent.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol · 2010
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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A neural autoregressive topic model
H. Larochelle and S. Laulyi · 2012
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Beta-negative binomial process and Poisson factor analysis
M. Zhou, L. Hannah, D. Dunson, and L. Carin · 2012
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Rectifier nonlinearities improve neural network acoustic models
A. L. Maas, A. Y. Hannun, and A. Y. Ng · 2013
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Auto-encoding variational Bayes
D. P. Kingma and M. Welling · 2014
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Semi-supervised learning with deep generative models
D.P. Kingma, D.J. Rezende, S. Mohamed, and M. Welling · 2014
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Neural variational inference and learning in belief networks
A. Mnih and K. Gregor · 2014
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Stochastic backpropagation and approximate inference in deep generative models
D. J. Rezende, S. Mohamed, and D. Wierstra · 2014
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-fei · 2014
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Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Scalable deep poisson factor analysis for topic modeling
Z. Gan, C. Chen, R. Henao, D. Carlson, and L. Carin · 2015
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Draw: A recurrent neural network for image generation
K. Gregor, I. Danihelka, A. Graves, and D. Wierstra · 2015
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2015
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Generative deep deconvolutional learning
Y. Pu, X. Yuan, and L. Carin · 2015
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Deep exponential families
R. Ranganath, L. Tang, L. Charlin, and D. M.Blei · 2015
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Semi-supervised learning with ladder networks
A. Rasmus, M. Berglund, M. Honkala, H. Valpola, and T. Raiko · 2015
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Variational inference with normalizing flows
D.J. Rezende and S. Mohamed · 2015
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Striving for simplicity: The all convolutional net
Neural variational inference for text processing
Y. Miao, L. Yu, and Phil Blunsomi · 2016
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Variational inference for monte carlo objectives
A. Mnih and D. J. Rezende · 2016
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Pixel recurrent neural network
A. Oord, N. Kalchbrenner, and K. Kavukcuoglu · 2016
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Variational autoencoder for deep learning of images, labels and captions
Y. Pu, Z. Gan, R. Henao, X. Yuan, C. Li, A. Stevens, and L. Carin · 2016
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A deep generative deconvolutional image model
Y. Pu, X. Yuan, A. Stevens, C. Li, and L. Carin · 2016
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Hierarchical variational models
R. Ranganath, D. Tran, and D. M. Blei · 2016
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Importance weighted autoencoders
Y. Burda, R. Grosse, and R. Salakhutdinov · 2016
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Variational gaussian copula inference
S. Han, X. Liao, D.B. Dunson, and L. Carin · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and Sun J · 2016
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Improving variational inference with inverse autoregressive flow
D. P. Kingma, T. Salimans, R. Jozefowicz, X.i Chen, I. Sutskever, and M. Welling · 2016
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Stein variational gradient descent: A general purpose bayesian inference algorithm
Q. Liu and D. Wang · 2016
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Symmetric variational autoencoder and connections to adversarial learning
L. Chen, S. Dai, Y. Pu, C. Li, and Q. Su L. Carin · 2017
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Learning to draw samples with amortized stein variational gradient descent
Y. Feng, D. Wang, and Q. Liu · 2017
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Stein variational adaptive importance sampling
J. Han and Q. Liu · 2017
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Adversarial symmetric variational autoencoder
Y. Pu, W. Wang, R. Henao, L. Chen, Z. Gan, C. Li, and Lawrence Carin · 2017
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Deconvolutional latent-variable model for text sequence matching
D. Shen, Y. Zhang, R. Henao, Q. Su, and L. Carin · 2017
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Deconvolutional paragraph representation learning
Y. Zhang, D. Shen, G. Wang, Z. Gan, R. Henao, and L. Carin · 2017
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