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We introduce a new general identifiable framework for principled disentanglement referred to as Structured Nonlinear Independent Component Analysis (SNICA).
On state estimation in switching environments
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A blind source separation technique based on second order statistics
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Nonlinear independent component analysis: Existence and uniqueness results
Hyvärinen, A. and Pajunen, P. (1999) · 1999
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Variational learning for switching state-space models
Ghahramani, Z. and Hinton, G. E. (2000) · 2000
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Independent Component Analysis
Hyvärinen, A., Karhunen, J., and Oja, E. (2001) · 2001
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Complex Analysis
Stein, E. and Shakarchi, R. (2003) · 2003
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Theory of point estimation
Lehmann, E. L. and Casella, G. (2006) · 2006
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Investigating the electrophysiological basis of resting state networks using magnetoencephalography
Brookes, M. J., Woolrich, M., Luckhoo, H., Price, D., Hale, J. R., Stephenson, M. C., Barnes, G. R., Smith, S. M., and Morris, P. G. (2011) · 2011
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Underspecification presents challenges for credibility in modern machine learning
D’Amour, A., Heller, K. A., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D., Hormozdiari, F., Houlsby, N., Hou, S., Jerfel, G., Karthikesalingam, A., Lucic, M., Ma, Y., McLean, C. Y., Mincu, D., Mitani, A., Montanari, A., Nado, Z., Natarajan, V., Nielson, C., Osborne, T. F., Raman, R., Ramasamy, K., Sayres, R., Schrouff, J., Seneviratne, M., Sequeira, S., Suresh, H., Veitch, V., Vladymyrov, M., Wang, X., Webster, K., Yadlowsky, S., Yun, T., Zhai, X., and Sculley, D. (2020) · 2011
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Meg and eeg data analysis with mne-python
Gramfort, A., Luessi, M., Larson, E., Engemann, D. A., Strohmeier, D., Brodbeck, C., Goj, R., Jas, M., Brooks, T., Parkkonen, L., et al. (2013) · 2013
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Fast transient networks in spontaneous human brain activity
Baker, A. P., Brookes, M. J., Rezek, I. A., Smith, S. M., Behrens, T., Smith, P. J. P., and Woolrich, M. (2014) · 2014
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2014) · 2014
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The cambridge centre for ageing and neuroscience (cam-can) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing
Shafto, M. A., Tyler, L. K., Dixon, M., Taylor, J. R., Rowe, J. B., Cusack, R., Calder, A. J., Marslen-Wilson, W. D., Duncan, J., Dalgleish, T., et al. (2014) · 2014
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An extension of slow feature analysis for nonlinear blind source separation
Sprekeler, H., Zito, T., and Wiskott, L. (2014) · 2014
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Inference in finite state space non parametric hidden Markov models and applications
Gassiat, E., Cleynen, A., and Robin, S. (2016) · 2016
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Unsupervised feature extraction by time-contrastive learning and nonlinear ICA
Hyvärinen, A. and Morioka, H. (2016) · 2016
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Consistent order estimation for nonparametric hidden Markov models
Lehéricy, L. (2019) · 2019
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Challenging common assumptions in the unsupervised learning of disentangled representations
Locatello, F., Bauer, S., Lucic, M., Raetsch, G., Gelly, S., Schölkopf, B., and Bachem, O. (2019) · 2019
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Causal discovery with general non-linear relationships using non-linear ICA
Monti, R. P., Zhang, K., and Hyvärinen, A. (2019) · 2019
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Relative gradient optimization of the jacobian term in unsupervised deep learning
Gresele, L., Fissore, G., Javaloy, A., Schölkopf, B., and Hyvärinen, A. (2020) · 2020
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Hidden Markov nonlinear ICA: Unsupervised learning from nonstationary time series
Hälvä, H. and Hyvärinen, A. (2020) · 2020
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Weakly-supervised disentanglement without compromises
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Composing graphical models with neural networks for structured representations and fast inference
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Look, listen and learn
Arandjelovic, R. and Zisserman, A. (2017) · 2017
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beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A. (2017) · 2017
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Nonlinear ICA of temporally dependent stationary sources
Hyvärinen, A. and Morioka, H. (2017) · 2017
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The cambridge centre for ageing and neuroscience (cam-can) data repository: Structural and functional mri, meg, and cognitive data from a cross-sectional adult lifespan sample
Taylor, J. R., Williams, N., Cusack, R., Auer, T., Shafto, M. A., Dixon, M., Tyler, L. K., Henson, R. N., et al. (2017) · 2017
Cited alongside, same era.
Brain network dynamics are hierarchically organized in time
Vidaurre, D., Smith, S. M., and Woolrich, M. W. (2017) · 2017
Cited alongside, same era.
Frequentist consistency of variational bayes
Wang, Y. and Blei, D. M. (2018) · 2018
Cited alongside, same era.
Locatello, F., Poole, B., Raetsch, G., Schölkopf, B., Bachem, O., and Tschannen, M. (2020) · 2020
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Nonlinear ica of fmri reveals primitive temporal structures linked to rest, task, and behavioral traits
Morioka, H., Calhoun, V., and Hyvärinen, A. (2020) · 2020
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Causal mosaic: Cause-effect inference via nonlinear ica and ensemble method
Wu, P. and Fukumizu, K. (2020) · 2020
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Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-vae
Zhou, D. and Wei, X.-X. (2020) · 2020
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Uncovering the structure of clinical EEG signals with self-supervised learning
Banville, H., Chehab, O., Hyvärinen, A., Engemann, D.-A., and Gramfort, A. (2021) · 2021
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Towards nonlinear disentanglement in natural data with temporal sparse coding
Klindt, D. A., Schott, L., Sharma, Y., Ustyuzhaninov, I., Brendel, W., Bethge, M., and Paiton, D. M. (2021) · 2021
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Independent innovation analysis for nonlinear vector autoregressive process
Morioka, H., Hälvä, H., and Hyvärinen, A. (2021) · 2021
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Nonlinear independent component analysis for continuous-time signals
Oberhauser, H. and Schell, A. (2021) · 2021
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