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Variational autoencoders often assume isotropic Gaussian priors and mean-field posteriors, hence do not exploit structure in scenarios where we may expect similarity or consistency across latent variables.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data
Neil D Lawrence · 2004
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Learning structured output representation using deep conditional generative models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan · 2015
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Structured inference networks for nonlinear state space models
Rahul G Krishnan, Uri Shalit, and David Sontag · 2016
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A disentangled recognition and nonlinear dynamics model for unsupervised learning
Marco Fraccaro, Simon Kamronn, Ulrich Paquet, and Ole Winther · 2017
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Gaussian process prior variational autoencoders
Francesco Paolo Casale, Adrian Dalca, Luca Saglietti, Jennifer Listgarten, and Nicolo Fusi · 2018
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Neural scene representation and rendering
SM Ali Eslami, Danilo Jimenez Rezende, Frederic Besse, Fabio Viola, Ari S Morcos, Marta Garnelo, Avraham Ruderman, Andrei A Rusu, Ivo Danihelka, Karol Gregor, et al · 2018
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Som-vae: Interpretable discrete representation learning on time series
Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, and Gunnar Rätsch · 2018
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Disentangled sequential autoencoder
Yingzhen Li and Stephan Mandt · 2018
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Danilo Jimenez Rezende and Fabio Viola · 2018
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On the fairness of disentangled representations
Francesco Locatello, Gabriele Abbati, Thomas Rainforth, Stefan Bauer, Bernhard Schölkopf, and Olivier Bachem · 2019
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Are disentangled representations helpful for abstract visual reasoning?
Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, and Olivier Bachem · 2019
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Sparse gaussian process variational autoencoders
Matthew Ashman, Jonathan So, Will Tebbutt, Vincent Fortuin, Michael Pearce, and Richard E. Turner · 2020
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Gp-vae: Deep probabilistic time series imputation
Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, and Stephan Mandt · 2020
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Scalable gaussian process variational autoencoders
Metod Jazbec, Vincent Fortuin, Michael Pearce, Stephan Mandt, and Gunnar Rätsch · 2020
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VAE with a vampprior
Jakub M. Tomczak and Max Welling · 2018
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Gaussian processes for machine learning , volume 2
Christopher KI Williams and Carl Edward Rasmussen · 2018
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Andreas Kopf, Vincent Fortuin, Vignesh Ram Somnath, and Manfred Claassen · 2019
Cited alongside, same era.
The gaussian process prior vae for interpretable latent dynamics from pixels
Michael Pearce · 2020
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Is independence all you need? on the generalization of representations learned from correlated data
Frederik Träuble, Elliot Creager, Niki Kilbertus, Anirudh Goyal, Francesco Locatello, Bernhard Schölkopf, and Stefan Bauer · 2020
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