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Brittle optimization has been observed to adversely impact model likelihoods for regression and VAEs when simultaneously fitting neural network mappings from a (random) variable onto the mean and variance of a dependent Gaussian variable.
Functional variational bayesian neural networks
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Mixture density networks
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Estimating the mean and variance of the target probability distribution
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Sparse gaussian processes using pseudo-inputs
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Bayesian data analysis
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2013) · 2013
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Fixed-form variational posterior approximation through stochastic linear regression
Salimans, T., Knowles, D. A., et al. (2013) · 2013
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Black box variational inference
Ranganath, R., Gerrish, S., and Blei, D. (2014) · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Variational inference: A review for statisticians
Blei, D. M., Kucukelbir, A., and McAuliffe, J. D. (2017) · 2017
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Tomczak, J. M. and Welling, M. (2017) · 2017
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Neural discrete representation learning
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Implicit reparameterization gradients
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Handling incomplete heterogeneous data using vaes
Nazabal, A., Olmos, P. M., Ghahramani, Z., and Valera, I. (2018) · 2018
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Student-t variational autoencoder for robust density estimation
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Rezende, D. J., Mohamed, S., and Wierstra, D. (2014) · 2014
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Hernández-Lobato, J. M. and Adams, R. (2015) · 2015
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Ghavamzadeh, M., Mannor, S., Pineau, J., and Tamar, A. (2016) · 2016
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Miwae: Deep generative modelling and imputation of incomplete data
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Generating diverse high-fidelity images with vq-vae-2
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