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This paper proposes Dirichlet Variational Autoencoder (DirVAE) using a Dirichlet prior for a continuous latent variable that exhibits the characteristic of the categorical probabilities.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Choice of basis for laplace approximation
D. J. C. MacKay · 1998
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Estimating a dirichlet distribution
T. Minka · 2000
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Gibbs sampling methods for stick-breaking priors
H. Ishwaran and L. F. James · 2001
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Combinatorial stochastic processes
J. Pitman · 2002
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Latent dirichlet allocation
D. M. Blei, A. Y. Ng, and M. I. Jordan · 2003
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Visualizing data using t-sne
L. V. D. Maaten and G. Hinton · 2008
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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
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Rectified linear units improve restricted boltzmann machines
V. Nair and G. Hinton · 2010
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One-shot learning by inverting a compositional causal process
B. M. Lake, R. R. Salakhutdinov, and J. Tenenbaum · 2013
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Machine reading tea leaves: Automatically evaluating topic coherence and topic model quality
J. H. Lau, D. Newman, and T. Baldwin · 2014
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Stochastic gradient variational bayes for gamma approximating distributions
D. A. Knowles · 2015
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Variational inference with normalizing flows
D. J. Rezende and S. Mohamed · 2015
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Elbo surgery: yet another way to carve up the variational evidence lower bound
M. Hoffman and M. Johnson · 2016
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Neural variational inference for text processing
Y. Miao, L. Yu, and P. Blunsom · 2016
Categorical reparameterization with gumbel-softmax
E. Jang, S. Gu, and B. Poole · 2017
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Variational deep embedding: An unsupervised and generative approach to clustering
Z. Jiang, Y. Zheng, H. Tan, B. Tang, and H. Zhou · 2017
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The concrete distribution: A continuous relaxation of discrete random variables
C. J. Maddison, A. Mnih, and Y. W. Teh · 2017
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Discovering discrete latent topics with neural variational inference
Y. Miao, E. Grefenstette, and P. Blunsom · 2017
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Stick-breaking variational autoencoders
E. Nalisnick and P. Smyth · 2017
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Discrete variational autoencoders
J. T. Rolfe · 2017
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Approximate inference for deep latent gaussian mixtures
E. Nalisnick, L. Hertel, and P. Smyth · 2016
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Ladder variational autoencoders
C. K. Sønderby, T. Raiko, L. Maaløe, S. K. Sønderby, and O. Winther · 2016
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba
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Auto-encoding variational bayes
D. P. Kingma and M. Welling
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Efficient gradient-based inference through transformations between bayes nets and neural nets
D. P. Kingma and M. Welling
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Semi-supervised learning with deep generative models
D. P. Kingma, S. Mohamed, D. J. Rezende, , and M. Welling
Cited in the paper.
Autoencoding variational inference for topic models
A. Srivastava and C. Sutton · 2017
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Whai: Weibull hybrid autoencoding inference for deep topic modeling
H. Zhang, B. Chen, D. Guo, and M. Zhou · 2018
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