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Variational autoencoders model high-dimensional data by positing low-dimensional latent variables that are mapped through a flexible distribution parametrized by a neural network.
Lagging inference networks and posterior collapse in variational autoencoders
He, J., Spokoyny, D., Neubig, G., & Berg-Kirkpatrick, T. (2019) · 1901
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Preventing posterior collapse with delta-VAEs
Razavi, A., Oord, A. v. d., Poole, B., & Vinyals, O. (2019) · 1901
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Lagging inference networks and posterior collapse in variational autoencoders
He, J., Spokoyny, D., Neubig, G., & Berg-Kirkpatrick, T. (2019) · 1901
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Cyclical annealing schedule: A simple approach to mitigating KL vanishing
Fu, H., Li, C., et al. (2019) · 1903
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Dueling decoders: Regularizing variational autoencoder latent spaces
Seybold, B., Fertig, E., Alemi, A., & Fischer, I. (2019) · 1905
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Variational autoencoders and nonlinear ICA: A unifying framework
Khemakhem, I., Kingma, D. P., & Hyvärinen, A. (2019) · 1907
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Optimal transport mapping via input convex neural networks
Makkuva, A. V., Taghvaei, A., Oh, S., & Lee, J. D. (2019) · 1908
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A surprisingly effective fix for deep latent variable modeling of text
Li, B., He, J., Neubig, G., Berg-Kirkpatrick, T., & Yang, Y. (2019) · 1909
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The usual suspects? Reassessing blame for VAE posterior collapse
Dai, B., Wang, Z., & Wipf, D. (2019) · 1912
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Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood
Raue, A., Kreutz, C., et al. (2009) · 1929
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Joining forces of Bayesian and frequentist methodology: a study for inference in the presence of non-identifiability
Raue, A., Kreutz, C., Theis, F. J., & Timmer, J. (2013) · 1984
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Identifiability in Stochastic Models: Characterization of Probability Distributions
Rao, B. & Prakasa, R. (1992) · 1992
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Introduction to linear algebra
Strang, G., Strang, G., Strang, G., & Strang, G. (1993) · 1993
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Existence and uniqueness of monotone measure-preserving maps
McCann, R. J. et al. (1995) · 1995
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Existence and uniqueness of monotone measure-preserving maps
McCann, R. J. et al. (1995) · 1995
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Long short-term memory
Hochreiter, S. & Schmidhuber, J. (1997) · 1997
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Revising beliefs in nonidentified models
Poirier, D. J. (1998) · 1998
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Em algorithms for pca and spca
Roweis, S. (1998) · 1998
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A unifying review of linear gaussian models
Roweis, S. & Ghahramani, Z. (1999) · 1999
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Probabilistic principal component analysis
Tipping, M. E. & Bishop, C. M. (1999) · 1999
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A generalization of principal components analysis to the exponential family
Collins, M., Dasgupta, S., & Schapire, R. E. (2001) · 2001
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An elementary introduction to monotone transportation
Ball, K. (2004) · 2004
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Preventing posterior collapse with Levenshtein variational autoencoder
Havrylov, S. & Titov, I. (2020) · 2004
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Discretized bottleneck in VAE: Posterior-collapse-free sequence-to-sequence learning
Zhao, Y., Yu, P., Mahapatra, S., Su, Q., & Chen, C. (2020) · 2004
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An elementary introduction to monotone transportation
Ball, K. (2004) · 2004
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Measures of Bayesian learning and identifiability in hierarchical models
Xie, Y. & Carlin, B. P. (2006) · 2006
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Gaussian mixture models
Reynolds, D. A. (2009) · 2009
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MNIST handwritten digit database
LeCun, Y., Cortes, C., & Burges, C. (2010) · 2010
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Bayesian identifiability: Contributions to an inconclusive debate
San Martın, E. & González, J. (2010) · 2010
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Five lectures on optimal transportation: geometry, regularity and applications
McCann, R. J. & Guillen, N. (2011) · 2011
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Density estimation using real NVP
Dinh, L., Sohl-Dickstein, J., & Bengio, S. (2016) · 2016
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β \beta -VAE: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., et al. (2016) · 2016
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Alemi, A. A., Poole, B., et al. (2017) · 2017
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Input convex neural networks
Amos, B., Xu, L., & Kolter, J. Z. (2017) · 2017
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Neural discrete representation learning
Oord, A. v. d., Vinyals, O., & Kavukcuoglu, K. (2017) · 2017
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Five lectures on optimal transportation: geometry, regularity and applications
McCann, R. J. & Guillen, N. (2011) · 2011
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Semi-supervised learning with deep generative models
Kingma, D. P., Mohamed, S., Rezende, D. J., & Welling, M. (2014) · 2014
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Auto-encoding variational Bayes
Kingma, D. P. & Welling, M. (2014) · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., & Wierstra, D. (2014) · 2014
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Auto-encoding variational Bayes
Kingma, D. P. & Welling, M. (2014) · 2014
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Importance weighted autoencoders
Burda, Y., Grosse, R., & Salakhutdinov, R. (2015) · 2015
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Tomczak, J. M. & Welling, M. (2017) · 2017
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Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., & Vollgraf, R. (2017) · 2017
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Improved variational autoencoders for text modeling using dilated convolutions
Yang, Z., Hu, Z., Salakhutdinov, R., & Berg-Kirkpatrick, T. (2017) · 2017
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Tackling over-pruning in variational autoencoders
Yeung, S., Kannan, A., Dauphin, Y., & Fei-Fei, L. (2017) · 2017
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Optimal control via neural networks: A convex approach
Chen, Y., Shi, Y., & Zhang, B. (2018) · 2018
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Avoiding latent variable collapse with generative skip models
Dieng, A. B., Kim, Y., Rush, A. M., & Blei, D. M. (2018) · 2018
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Semi-amortized variational autoencoders
Kim, Y., Wiseman, S., Miller, A., Sontag, D., & Rush, A. (2018) · 2018
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Unsupervised discrete sentence representation learning for interpretable neural dialog generation
Zhao, T., Lee, K., & Eskenazi, M. (2018) · 2018
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Semi-amortized variational autoencoders
Kim, Y., Wiseman, S., Miller, A., Sontag, D., & Rush, A. (2018) · 2018
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Variational autoencoders and the variable collapse phenomenon
Asperti, A. (2019) · 2019
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Don’t blame the ELBO! A linear VAE perspective on posterior collapse
Lucas, J., Tucker, G., Grosse, R. B., & Norouzi, M. (2019) · 2019
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BIVA: A very deep hierarchy of latent variables for generative modeling
Maalø e, L., Fraccaro, M., Liévin, V., & Winther, O. (2019) · 2019
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Computational optimal transport
Peyré, G., Cuturi, M., et al. (2019) · 2019
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Don’t blame the ELBO! A linear VAE perspective on posterior collapse
Lucas, J., Tucker, G., Grosse, R. B., & Norouzi, M. (2019) · 2019
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ICE-BeeM: Identifiable conditional energy-based deep models based on nonlinear ICA
Khemakhem, I., Monti, R. P., Kingma, D. P., & Hyvarinen, A. (2020) · 2020
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On implicit regularization in
Kumar, A. & Poole, B. (2020) · 2020
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Characterizing and avoiding problematic global optima of variational autoencoders
Yacoby, Y., Pan, W., & Doshi-Velez, F. (2020) · 2020
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Identifying Bayesian mixture models
Betancourt, M. (2017) · 2021
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On structural and practical identifiability
Wieland, F.-G., Hauber, A. L., Rosenblatt, M., Tönsing, C., & Timmer, J. (2021) · 2021
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