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In variational autoencoders, the prior on the latent codes $z$ is often treated as an afterthought, but the prior shapes the kind of latent representation that the model learns.
The coalescent
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Probabilistic principal component analysis
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Clustering with instance-level constraints
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Latent Dirichlet allocation
Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003) · 2003
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Neal, R. M. (2003) · 2003
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Griffiths, T. L., Jordan, M. I., Tenenbaum, J. B., and Blei, D. M. (2004) · 2004
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Sparse gaussian processes using pseudo-inputs
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Reading tea leaves: How humans interpret topic models
Chang, J., Gerrish, S., Wang, C., Boyd-Graber, J. L., and Blei, D. M. (2009) · 2009
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MNIST handwritten digit database
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The time-marginalized coalescent prior for hierarchical clustering
Boyles, L. and Welling, M. (2012) · 2012
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Kingma, D. P. and Welling, M. (2014) · 2014
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Composing graphical models with neural networks for structured representations and fast inference
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Nonparametric variational auto-encoders for hierarchical representation learning
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Masked autoregressive flow for density estimation
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Pixelcnn++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications
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Dvae#: Discrete variational autoencoders with relaxed Boltzmann priors
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