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An old problem in multivariate statistics is that linear Gaussian models are often unidentifiable, i.e.
On differentiable mappings
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Sparse coding with an overcomplete basis set: A strategy employed by V1?
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Learning the parts of objects by non-negative matrix factorization
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The three easy routes to independent component analysis: contrasts and geometry
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Independent Component Analysis
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A Simple Framework for Contrastive Learning of Visual Representations
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Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data
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When does non-negative matrix factorization give a correct decomposition into parts?
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Identifiability, separability, and uniqueness of linear ica models
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Non-negative matrix factorization with sparseness constraints
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Hidden Markov nonlinear ICA: Unsupervised learning from nonstationary time series
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On implicit regularization in β \beta -vaes
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Robust contrastive learning and nonlinear ICA in the presence of outliers
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Causality
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On the identifiability of the post-nonlinear causal model
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Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis
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Investigating the electrophysiological basis of resting state networks using magnetoencephalography
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Disentangling identifiable features from noisy data with structured nonlinear ICA
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When is unsupervised disentanglement possible?
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Causal autoregressive flows
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Identifiable variational autoencoders via sparse decoding
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Independent innovation analysis for nonlinear vector autoregressive process
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I Don’t Need u: Identifiable Non-Linear ICA Without Side Information
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Contrastive learning inverts the data generating process
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Function classes for identifiable nonlinear independent component analysis
Buchholz, S., Besserve, M., and Schölkopf, B. (2022) · 2022
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Binary independent component analysis: A non-stationarity-based approach
Hyttinen, A., Barin-Pacela, V., and Hyvärinen, A. (2022) · 2022
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On the identifiability and estimation of causal location-scale noise models
Immer, A., Schultheiss, C., Vogt, J. E., Schölkopf, B., Bühlmann, P., and Marx, A. (2022) · 2022
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Structure learning for directed trees
Jakobsen, M. E., Shah, R. D., Bühlmann, P., and Peters, J. (2022) · 2022
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Identifiability of deep generative models under mixture priors without auxiliary information
Kivva, B., Rajendran, G., Ravikumar, P., and Aragam, B. (2022) · 2022
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Disentanglement via mechanism sparsity regularization: A new principle for nonlinear ICA
Lachapelle, S., Rodriguez, P., Sharma, Y., Everett, K. E., Le Priol, R., Lacoste, A., and Lacoste-Julien, S. (2022) · 2022
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Identifying patient-specific root causes with the heteroscedastic noise model
Strobl, E. V. and Lasko, T. A. (2022) · 2022
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