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In this paper, we show that slow feature analysis (SFA), a common time series decomposition method, naturally fits into the flow-based models (FBM) framework, a type of invertible neural latent variable models.
Contrastive learning and neural oscillations
Baldi, P. and Pineda, F · 1991
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Indeterminacy and identifiability of blind identification
Tong, L., Liu, R.-W., Soon, V. C., and Huang, Y.-F · 1991
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Nonlinear independent component analysis: Existence and uniqueness results
Hyvärinen, A. and Pajunen, P · 1999
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Independent component analysis: algorithms and applications
Hyvärinen, A. and Oja, E · 2000
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Methods using time structure
Hyvärinen, A., Karhunen, J., and Oja, E · 2001
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Independent slow feature analysis and nonlinear blind source separation
Blaschke, T., Zito, T., and Wiskott, L · 2007
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A maximum-likelihood interpretation for slow feature analysis
Turner, R. and Sahani, M · 2007
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Slow feature analysis: Unsupervised learning of invariances
Wiskott, L. and Sejnowski, T. J · 2007
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Regularized sparse kernel slow feature analysis
Böhmer, W., Grünewälder, S., Nickisch, H., and Obermayer, K · 2011
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Kingma, D. P. and Welling, M · 2013
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Dinh, L., Krueger, D., and Bengio, Y · 2014
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An extension of slow feature analysis for nonlinear blind source separation
Sprekeler, H., Zito, T., and Wiskott, L · 2014
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Variational inference with normalizing flows
Rezende, D. J. and Mohamed, S · 2015
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Spectral inference networks: Unifying deep and spectral learning
Pfau, D., Petersen, S., Agarwal, A., Barrett, D. G., and Stachenfeld, K. L · 2018
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Gradient-based training of slow feature analysis by differentiable approximate whitening
Schüler, M., Hlynsson, H. D., and Wiskott, L · 2018
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Recent advances in autoencoder-based representation learning
Tschannen, M., Bachem, O., and Lucic, M · 2018
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Unsupervised deep slow feature analysis for change detection in multi-temporal remote sensing images
Du, B., Ru, L., Wu, C., and Zhang, L · 2019
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Nonlinear ica using auxiliary variables and generalized contrastive learning
Hyvarinen, A., Sasaki, H., and Turner, R · 2019
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Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2016
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Hyvarinen, A. and Morioka, H · 2016
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Nonlinear ica of temporally dependent stationary sources
Hyvarinen, A. and Morioka, H · 2017
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Variational autoencoders and nonlinear ica: A unifying framework
Khemakhem, I., Kingma, D. P., and Hyvärinen, A · 2019
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Normalizing flows for probabilistic modeling and inference
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., and Lakshminarayanan, B · 2019
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Disentanglement by nonlinear ica with general incompressible-flow networks (gin)
Sorrenson, P., Rother, C., and Köthe, U · 2020
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