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Nonlinear independent component analysis (ICA) aims to recover the underlying independent latent sources from their observable nonlinear mixtures.
Does the inertia of a body depend upon its energy-content
A. Einstein · 1905
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Analyse des liaisons de probabilité
G. Darmois · 1951
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The nature of the natural sciences
L. K. Nash · 1963
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Liouville’s theorem on conformal mapping
H. Flanders · 1966
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Orthographic analysis of geological structures—I. deformation theory
D. G. De Paor · 1983
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Independent component analysis, a new concept?
P. Comon · 1994
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A fast fixed-point algorithm for independent component analysis
A. Hyvärinen and E. Oja · 1997
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Nonlinear independent component analysis: Existence and uniqueness results
A. Hyvärinen and P. Pajunen · 1999
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Source separation in post-nonlinear mixtures
A. Taleb and C. Jutten · 1999
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Overdetermined blind source separation: Using more sensors than source signals in a noisy mixture
M. Joho, H. Mathis, and R. H. Lambert · 2000
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Causation, prediction, and search
P. Spirtes, C. N. Glymour, R. Scheines, and D. Heckerman · 2000
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Unsupervised learning in neural computation
E. Oja · 2002
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Optimally sparse representation in general (nonorthogonal) dictionaries via L 1 L_{1} minimization
D. L. Donoho and M. Elad · 2003
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ICA with sparse connections: Revisited
K. Zhang, H. Peng, L. Chan, and A. Hyvärinen · 2009
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Inverse dynamics control of floating base systems using orthogonal decomposition
M. Mistry, J. Buchli, and S. Schaal · 2010
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A comparison of three occam’s razors for markovian causal models
J. Zhang · 2013
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Density estimation using Real NVP
L. Dinh, J. Sohl-Dickstein, and S. Bengio · 2016
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Unsupervised feature extraction by time-contrastive learning and nonlinear ICA
A. Hyvärinen and H. Morioka · 2016
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Explorability and the origin of network sparsity in living systems
D. M. Busiello, S. Suweis, J. Hidalgo, and A. Maritan · 2017
Hidden markov nonlinear ICA: Unsupervised learning from nonstationary time series
H. Hälvä and A. Hyvärinen · 2020
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Variational autoencoders and nonlinear ICA: A unifying framework
I. Khemakhem, D. Kingma, R. Monti, and A. Hyvärinen · 2020
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Vintage factor analysis with varimax performs statistical inference
K. Rohe and M. Zeng · 2020
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Disentanglement by nonlinear ICA with general incompressible-flow networks (GIN)
P. Sorrenson, C. Rother, and U. Köthe · 2020
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Independent mechanism analysis, a new concept?
L. Gresele, J. von Kügelgen, V. Stimper, B. Schölkopf, and M. Besserve · 2021
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Nonlinear ICA of temporally dependent stationary sources
A. Hyvärinen and H. Morioka · 2017
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Glow: Generative flow with invertible 1 1 x 1 1 convolutions
D. P. Kingma and P. Dhariwal · 2018
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Learning directed acyclic graph models based on sparsest permutations
G. Raskutti and C. Uhler · 2018
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Nonlinear ICA using auxiliary variables and generalized contrastive learning
A. Hyvärinen, H. Sasaki, and R. Turner · 2019
Cited alongside, same era.
Challenging common assumptions in the unsupervised learning of disentangled representations
F. Locatello, S. Bauer, M. Lucic, G. Raetsch, S. Gelly, B. Schölkopf, and O. Bachem · 2019
Cited alongside, same era.
Adaptive estimation in structured factor models with applications to overlapping clustering
X. Bing, F. Bunea, Y. Ning, and M. Wegkamp · 2020
Cited alongside, same era.
Disentangling identifiable features from noisy data with structured nonlinear ICA
H. Hälvä, S. Le Corff, L. Lehéricy, J. So, Y. Zhu, E. Gassiat, and A. Hyvärinen · 2021
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Identifiable variational autoencoders via sparse decoding
G. E. Moran, D. Sridhar, Y. Wang, and D. M. Blei · 2021
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Local disentanglement in variational auto-encoders using jacobian l _ 1 l\_1 regularization
T. Rhodes and D. Lee · 2021
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I don’t need 𝐮 \mathbf{u} : Identifiable non-linear ICA without side information
M. Willetts and B. Paige · 2021
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Learning temporally causal latent processes from general temporal data
W. Yao, Y. Sun, A. Ho, C. Sun, and K. Zhang · 2021
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iVPF: Numerical invertible volume preserving flow for efficient lossless compression
S. Zhang, C. Zhang, N. Kang, and Z. Li · 2021
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Disentanglement via mechanism sparsity regularization: A new principle for nonlinear ICA
S. Lachapelle, P. R. López, Y. Sharma, K. Everett, R. L. Priol, A. Lacoste, and S. Lacoste-Julien · 2022
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Nonlinear ICA using volume-preserving transformations
X. Yang, Y. Wang, J. Sun, X. Zhang, S. Zhang, Z. Li, and J. Yan · 2022
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