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Identifiability, or recovery of the true latent representations from which the observed data originates, is de facto a fundamental goal of representation learning.
Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 1906
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Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 1906
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Nonlinear independent component analysis: Existence and uniqueness results
Aapo Hyvärinen and Petteri Pajunen · 1999
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Density estimation by dual ascent of the log-likelihood
Esteban G Tabak, Eric Vanden-Eijnden, et al · 2010
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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High-dimensional probability estimation with deep density models
Oren Rippel and Ryan Prescott Adams · 2013
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A family of nonparametric density estimation algorithms
Esteban G Tabak and Cristina V Turner · 2013
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Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
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Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
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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 Hyvarinen and Hiroshi Morioka · 2016
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Improved variational inference with inverse autoregressive flow
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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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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Nonlinear ica using auxiliary variables and generalized contrastive learning
Aapo Hyvarinen, Hiroaki Sasaki, and Richard E Turner · 2018
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Hyunjik Kim and Andriy Mnih · 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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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Variational inference of disentangled latent concepts from unlabeled observations
Abhishek Kumar, Prasanna Sattigeri, and Avinash Balakrishnan · 2017
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Density estimation in infinite dimensional exponential families
Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyvärinen, and Revant Kumar · 2017
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Thomas Müller, Brian McWilliams, Fabrice Rousselle, Markus Gross, and Jan Novák · 2018
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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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Normalizing flows: Introduction and ideas
Ivan Kobyzev, Simon Prince, and Marcus A Brubaker · 2019
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