Fetching the paper…
Reading the bibliography…
The framework of variational autoencoders allows us to efficiently learn deep latent-variable models, such that the model's marginal distribution over observed variables fits the data.
The Change-of-Variables Formula Using Matrix Volume
Ben-Israel, A. (1999) · 1999
Earlier work this paper cites.
Nonlinear independent component analysis: Existence and uniqueness results
Hyvärinen, A. and Pajunen, P. (1999) · 1999
Earlier work this paper cites.
Introduction to Smooth Manifolds
Lee, J. M. (2003) · 2003
Earlier work this paper cites.
Measuring statistical dependence with Hilbert-Schmidt norms
Gretton, A., Bousquet, O., Smola, A., and Schölkopf, B. (2005) · 2005
Earlier work this paper cites.
Auto-Encoding Variational Bayes
Kingma, D. P. and Welling, M. (2013) · 2013
Earlier work this paper cites.
NICE: Non-linear Independent Components Estimation
Dinh, L., Krueger, D., and Bengio, Y. (2014) · 2014
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Kingma, D. P. and Ba, J. (2014) · 2014
Earlier work this paper cites.
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
Rezende, D. J., Mohamed, S., and Wierstra, D. (2014) · 2014
Earlier work this paper cites.
Importance Weighted Autoencoders
Burda, Y., Grosse, R., and Salakhutdinov, R. (2015) · 2015
Earlier work this paper cites.
Long-term neural and physiological phenotyping of a single human
Poldrack, R. A., Laumann, T. O., Koyejo, O., Gregory, B., Hover, A., Chen, M.-Y., Gorgolewski, K. J., Luci, J., Joo, S. J., and Boyd, R. L. (2015) · 2015
Earlier work this paper cites.
Variational Inference with Normalizing Flows
Rezende, D. J. and Mohamed, S. (2015) · 2015
Earlier work this paper cites.
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. (2016) · 2016
Cited alongside, same era.
Unsupervised feature extraction by time-contrastive learning and nonlinear ICA
Hyvärinen, A. and Morioka, H. (2016) · 2016
Cited alongside, same era.
Improving Variational Inference with Inverse Autoregressive Flow
Kingma, D. P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., and Welling, M. (2016) · 2016
Cited alongside, same era.
Learning independent features with adversarial nets for non-linear ICA
Brakel, P. and Bengio, Y. (2017) · 2017
Cited alongside, same era.
Nonlinear ICA of temporally dependent stationary sources
Hyvärinen, A. and Morioka, H. (2017) · 2017
Cited alongside, same era.
Towards a Definition of Disentangled Representations
Higgins, I., Amos, D., Pfau, D., Racaniere, S., Matthey, L., Rezende, D., and Lerchner, A. (2018) · 2018
Later among the works it cites.
Kim, H. and Mnih, A. (2018) · 2018
Later among the works it cites.
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Locatello, F., Bauer, S., Lucic, M., Rätsch, G., Gelly, S., Schölkopf, B., and Bachem, O. (2018) · 2018
Later among the works it cites.
Disentangling Disentanglement in Variational Autoencoders
Mathieu, E., Rainforth, T., Siddharth, N., and Teh, Y. W. (2018) · 2018
Later among the works it cites.
Variational Autoencoders Pursue PCA Directions (by Accident)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Elements of Causal Inference: Foundations and Learning Algorithms
Peters, J., Janzing, D., and Schölkopf, B. (2017) · 2017
Cited alongside, same era.
Density Estimation in Infinite Dimensional Exponential Families
Sriperumbudur, B., Fukumizu, K., Gretton, A., Hyvärinen, A., and Kumar, R. (2017) · 2017
Cited alongside, same era.
Understanding disentangling in β \beta -VAE
Burgess, C. P., Higgins, I., Pal, A., Matthey, L., Watters, N., Desjardins, G., and Lerchner, A. (2018) · 2018
Cited alongside, same era.
Isolating Sources of Disentanglement in Variational Autoencoders
Chen, R. T. Q., Li, X., Grosse, R., and Duvenaud, D. (2018) · 2018
Cited alongside, same era.
Structured Disentangled Representations
Esmaeili, B., Wu, H., Jain, S., Bozkurt, A., Siddharth, N., Paige, B., Brooks, D. H., Dy, J., and van de Meent, J.-W. (2018) · 2018
Cited alongside, same era.
Rolinek, M., Zietlow, D., and Martius, G. (2018) · 2018
Later among the works it cites.
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning
Hyvärinen, A., Sasaki, H., and Turner, R. (2019) · 2019
Closest in time.
BIVA: A very deep hierarchy of latent variables for generative modeling
Maaløe, L., Fraccaro, M., Liévin, V., and Winther, O. (2019) · 2019
Closest in time.
Causal discovery with general non-linear relationships using non-linear ICA
Monti, R. P., Zhang, K., and Hyvarinen, A. (2019) · 2019
Closest in time.
Doubly reparameterized gradient estimators for monte carlo objectives
Tucker, G., Lawson, D., Gu, S., and Maddison, C. J. (2018) · 2020
Closest in time.
A Linear Non-Gaussian Acyclic Model for Causal Discovery
Shimizu, S., Hoyer, P. O., Hyvärinen, A., and Kerminen, A. (2006) · 2030
Closest in time.