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A central question of representation learning asks under which conditions it is possible to reconstruct the true latent variables of an arbitrarily complex generative process.
Independent component analysis, a new concept?
Pierre Comon · 1994
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Nonlinear independent component analysis: Existence and uniqueness results
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Deep unsupervised clustering with gaussian mixture variational autoencoders
Nat Dilokthanakul, Pedro AM Mediano, Marta Garnelo, Matthew CH Lee, Hugh Salimbeni, Kai Arulkumaran, and Murray Shanahan · 2016
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Density estimation using Real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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Unsupervised feature extraction by time-contrastive learning and nonlinear ICA
Aapo Hyvärinen and Hiroshi Morioka · 2016
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Composing graphical models with neural networks for structured representations and fast inference
Matthew J Johnson, David K Duvenaud, Alex Wiltschko, Ryan P Adams, and Sandeep R Datta · 2016
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EMNIST: an extension of MNIST to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and André van Schaik · 2017
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β \beta -VAE: Learning basic visual concepts with a constrained variational framework
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Nonlinear ICA using auxiliary variables and generalized contrastive learning
Aapo Hyvärinen, Hiroaki Sasaki, and Richard E Turner · 2018
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i-RevNet: Deep invertible networks
Jörn-Henrik Jacobsen, Arnold Smeulders, and Edouard Oyallon · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
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Challenging common assumptions in the unsupervised learning of disentangled representations
Francesco Locatello, Stefan Bauer, Mario Lucic, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem · 2018
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Guided image generation with conditional invertible neural networks
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Nonlinear ICA of temporally dependent stationary sources
Aapo Hyvärinen and Hiroshi Morioka · 2017
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Isolating sources of disentanglement in variational autoencoders
Tian Qi Chen, Xuechen Li, Roger B Grosse, and David K Duvenaud · 2018
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Learning disentangled joint continuous and discrete representations
Emilien Dupont · 2018
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Lynton Ardizzone, Carsten Lüth, Jakob Kruse, Carsten Rother, and Ullrich Köthe · 2019
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Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, and Pieter Abbeel · 2019
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Variational autoencoders and nonlinear ICA: A unifying framework
Ilyes Khemakhem, Diederik P Kingma, and Aapo Hyvärinen · 2019
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Variational autoencoder with truncated mixture of gaussians for functional connectivity analysis
Qingyu Zhao, Nicolas Honnorat, Ehsan Adeli, Adolf Pfefferbaum, Edith V Sullivan, and Kilian M Pohl · 2019
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