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We develop the sparse VAE for unsupervised representation learning on high-dimensional data.
On the fairness of disentangled representations
Locatello, F., Abbati, G., Rainforth, T., Bauer, S., Schölkopf, B., and Bachem, O. (2019a) · 1905
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Nonlinear factor analysis as a statistical method
Yalcin, I. and Amemiya, Y. (2001) · 2001
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Bayesian factor regression models in the “large p, small n” paradigm
Bernardo, J., Bayarri, M., Berger, J., Dawid, A., Heckerman, D., Smith, A., and West, M. (2003) · 2003
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Latent Dirichlet allocation
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Bolstad, B., Irizarry, R., Åstrand, M., and Speed, T. (2003) · 2003
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When does non-negative matrix factorization give a correct decomposition into parts?
Donoho, D. L. and Stodden, V. (2003) · 2003
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Identifiability, separability, and uniqueness of linear ICA models
Eriksson, J. and Koivunen, V. (2004) · 2004
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Vintage factor analysis with varimax performs statistical inference
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Infinite latent feature models and the Indian buffet process
Griffiths, T. L. and Ghahramani, Z. (2005) · 2005
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High-dimensional sparse factor modeling: applications in gene expression genomics
Carvalho, C. M., Chang, J., Lucas, J. E., Nevins, J. R., Wang, Q., and West, M. (2008) · 2008
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Sparse Bayesian infinite factor models
Bhattacharya, A. and Dunson, D. B. (2011) · 2011
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Nonparametric Bayesian sparse factor models with application to gene expression modeling
Knowles, D., Ghahramani, Z., et al. (2011) · 2011
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Zhou, D. and Wei, X.-X. (2020) · 2011
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A practical algorithm for topic modeling with provable guarantees
Arora, S., Ge, R., Halpern, Y., Mimno, D., Moitra, A., Sontag, D., Wu, Y., and Zhu, M. (2013) · 2013
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Representation learning: A review and new perspectives
Bengio, Y., Courville, A., and Vincent, P. (2013) · 2013
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Amortized inference in probabilistic reasoning
Gershman, S. and Goodman, N. (2014) · 2014
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M. (2014) · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D. (2014) · 2014
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The MovieLens Datasets: History and Context
Harper, F. M. and Konstan, J. A. (2015) · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2015) · 2015
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Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · 2015
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Asymptotic properties of Bayes risk of a general class of shrinkage priors in multiple hypothesis testing under sparsity
The spike-and-slab lasso
Ročková, V. and George, E. I. (2018) · 2018
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Variational autoencoders and nonlinear ICA: A unifying framework
Khemakhem, I., Kingma, D., Monti, R., and Hyvarinen, A. (2020) · 2020
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Weakly-supervised disentanglement without compromises
Locatello, F., Poole, B., Rätsch, G., Schölkopf, B., Bachem, O., and Tschannen, M. (2020) · 2020
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Variational sparse coding
Tonolini, F., Jensen, B. S., and Murray-Smith, R. (2020) · 2020
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Properties from mechanisms: An equivariance perspective on identifiable representation learning
Ahuja, K., Hartford, J., and Bengio, Y. (2021) · 2021
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On the transfer of disentangled representations in realistic settings
Dittadi, A., Träuble, F., Locatello, F., Wuthrich, M., Agrawal, V., Winther, O., Bauer, S., and Schölkopf, B. (2021) · 2021
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Ghosh, P., Tang, X., Ghosh, M., and Chakrabarti, A. (2016) · 2016
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Fast Bayesian factor analysis via automatic rotations to sparsity
Ročková, V. and George, E. I. (2016) · 2016
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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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oi-VAE: Output interpretable VAEs for nonlinear group factor analysis
Ainsworth, S. K., Foti, N. J., Lee, A. K., and Fox, E. B. (2018) · 2018
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Sparse-coding variational auto-encoders
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preprocessCore: A collection of pre-processing functions
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Bayesian linear regression with sparse priors
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Disentangling identifiable features from noisy data with structured nonlinear ICA
Hälvä, H., Corff, S. L., Lehéricy, L., So, J., Zhu, Y., Gassiat, E., and Hyvarinen, A. (2021) · 2021
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When is unsupervised disentanglement possible?
Horan, D., Richardson, E., and Weiss, Y. (2021) · 2021
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Disentanglement via mechanism sparsity regularization: A new principle for nonlinear ICA
Lachapelle, S., López, P. R., Sharma, Y., Everett, K., Priol, R. L., Lacoste, A., and Lacoste-Julien, S. (2021) · 2021
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An identifiable double VAE for disentangled representations
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Local disentanglement in variational auto-encoders using Jacobian l1 regularization
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On disentangled representations learned from correlated data
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Self-supervised learning with data augmentations provably isolates content from style
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Bayesian pyramids: Identifiable multilayer discrete latent structure models for discrete data
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Adaptive estimation in structured factor models with applications to overlapping clustering
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