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Posterior collapse in Variational Autoencoders (VAEs) arises when the variational posterior distribution closely matches the prior for a subset of latent variables.
Logistic-normal distributions: Some properties and uses
J. Atchison and S. M. Shen · 1980
Earlier work this paper cites.
Learning internal representations by error propagation
D. E. Rumelhart, G. E. Hinton, and R. J. Williams · 1985
Earlier work this paper cites.
Latent variable models and factors analysis
D. J. Bartholomew · 1987
Earlier work this paper cites.
Neural networks and principal component analysis: Learning from examples without local minima
P. Baldi and K. Hornik · 1989
Earlier work this paper cites.
The mnist database of handwritten digits
Y. LeCun · 1998
Earlier work this paper cites.
An introduction to variational methods for graphical models
M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul · 1999
Earlier work this paper cites.
Probabilistic principal component analysis
M. E. Tipping and C. M. Bishop · 1999
Earlier work this paper cites.
Information bottleneck for gaussian variables
G. Chechik, A. Globerson, N. Tishby, and Y. Weiss · 2005
Earlier work this paper cites.
Robust principal component analysis?
E. J. Candès, X. Li, Y. Ma, and J. Wright · 2011
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2015
Earlier work this paper cites.
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Earlier work this paper cites.
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