Fetching the paper…
Reading the bibliography…
Unsupervised learning of latent variable models (LVMs) is widely used to represent data in machine learning.
La géométrie des espaces de Riemann
E. Cartan · 1925
Earlier work this paper cites.
The condition for uniqueness of solutions of the dirichlet problem for the wave equation in coordinate rectangles
D. R. Dunninger and E. C. Zachmanoglou · 1967
Earlier work this paper cites.
Complex Analysis: An Introduction to the Theory of Analytic Functions of One Complex Variable, Third Edition
L. Ahlfors · 1979
Earlier work this paper cites.
Separation of variables on n-dimensional riemannian manifolds. i. the n-sphere sn and euclidean n-space rn
E. G. Kalnins and W. Miller · 1986
Earlier work this paper cites.
On a partial differential equation involving the jacobian determinant
B. Dacorogna and J. Moser · 1990
Earlier work this paper cites.
Independent component analysis, a new concept?
P. Comon · 1994
Earlier work this paper cites.
Learning in the presence of concept drift and hidden contexts
G. Widmer and M. Kubat · 1996
Earlier work this paper cites.
Mathematical Elasticity: Volume II: Theory of Plates
P. G. Ciarlet · 1997
Earlier work this paper cites.
Nonlinear independent component analysis: Existence and uniqueness results
A. Hyvärinen and P. Pajunen · 1999
Earlier work this paper cites.
Source separation in post-nonlinear mixtures
A. Taleb and C. Jutten · 1999
Earlier work this paper cites.
Nonlinear dimensionality reduction by locally linear embedding
S. T. Roweis and L. K. Saul · 2000
Earlier work this paper cites.
A global geometric framework for nonlinear dimensionality reduction
J. B. Tenenbaum, V. de Silva, and J. C. Langford · 2000
Earlier work this paper cites.
Geometric Function Theory and Non-linear Analysis
T. Iwaniec and G. J. Martin · 2001
Earlier work this paper cites.
A theorem on geometric rigidity and the derivation of nonlinear plate theory from three-dimensional elasticity
G. Friesecke, R. D. James, and S. Müller · 2002
Earlier work this paper cites.
Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data
D. L. Donoho and C. Grimes · 2003
Earlier work this paper cites.
Kernel-based nonlinear blind source separation
S. Harmeling, A. Ziehe, M. Kawanabe, and K. Müller · 2003
Earlier work this paper cites.
Introduction to Smooth Manifolds
J. M. Lee · 2003
Earlier work this paper cites.
Genus zero surface conformal mapping and its application to brain surface mapping
X. Gu, Y. Wang, T. F. Chan, P. M. Thompson, and S. Yau · 2004
Cited alongside, same era.
2d-shape analysis using conformal mapping
E. Sharon and D. Mumford · 2006
Cited alongside, same era.
Conformal geometry and its applications on 3d shape matching, recognition, and stitching
S. Wang, Y. Wang, M. Jin, X. D. Gu, and D. Samaras · 2007
Cited alongside, same era.
Partial differential equations
L. C. Evans · 2010
Cited alongside, same era.
Representation learning: A review and new perspectives
Y. Bengio, A. C. Courville, and P. Vincent · 2013
Cited alongside, same era.
A survey on concept drift adaptation
J. a. Gama, I. Žliobaitundefined, A. Bifet, M. Pechenizkiy, and A. Bouchachia · 2014
Cited alongside, same era.
Learning deep disentangled embeddings with the f-statistic loss
K. Ridgeway and M. C. Mozer · 2018
Later among the works it cites.
Nonlinear ICA using auxiliary variables and generalized contrastive learning
A. Hyvärinen, H. Sasaki, and R. E. Turner · 2019
Later among the works it cites.
Challenging common assumptions in the unsupervised learning of disentangled representations
F. Locatello, S. Bauer, M. Lucic, G. Rätsch, S. Gelly, B. Schölkopf, and O. Bachem · 2019
Later among the works it cites.
Variational autoencoders pursue PCA directions (by accident)
M. Rolínek, D. Zietlow, and G. Martius · 2019
Later among the works it cites.
nflows: normalizing flows in PyTorch, Nov. 2020
C. Durkan, A. Bekasov, I. Murray, and G. Papamakarios · 2020
Later among the works it cites.
Variational autoencoders and nonlinear ICA: A unifying framework
I. Khemakhem, D. P. Kingma, R. P. Monti, and A. Hyvärinen · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Generative Adversarial Networks
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Cited alongside, same era.
Auto-Encoding Variational Bayes
D. P. Kingma and M. Welling · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
Cited alongside, same era.
Variational inference with normalizing flows
D. J. Rezende and S. Mohamed · 2015
Cited alongside, same era.
Unsupervised feature extraction by time-contrastive learning and nonlinear ICA
A. Hyvärinen and H. Morioka · 2016
Cited alongside, same era.
beta-vae: Learning basic visual concepts with a constrained variational framework
I. Higgins, L. Matthey, A. Pal, C. P. Burgess, X. Glorot, M. M. Botvinick, S. Mohamed, and A. Lerchner · 2017
Cited alongside, same era.
Later among the works it cites.
On implicit regularization in β \beta -vaes
A. Kumar and B. Poole · 2020
Later among the works it cites.
Disentanglement by nonlinear ICA with general incompressible-flow networks (GIN)
P. Sorrenson, C. Rother, and U. Köthe · 2020
Later among the works it cites.
Mathematical Elasticity, Volume I: Three-Dimensional Elasticity
P. G. Ciarlet · 2021
Later among the works it cites.
Independent mechanism analysis, a new concept?
L. Gresele, J. von Kügelgen, V. Stimper, B. Schölkopf, and M. Besserve · 2021
Later among the works it cites.
When is unsupervised disentanglement possible?
D. Horan, E. Richardson, and Y. Weiss · 2021
Later among the works it cites.
Normalizing flows for probabilistic modeling and inference
G. Papamakarios, E. T. Nalisnick, D. J. Rezende, S. Mohamed, and B. Lakshminarayanan · 2021
Later among the works it cites.
Toward causal representation learning
B. Schölkopf, F. Locatello, S. Bauer, N. R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio · 2021
Later among the works it cites.
Demystifying inductive biases for (beta-)vae based architectures
D. Zietlow, M. Rolínek, and G. Martius · 2021
Later among the works it cites.
Nonlinear ICA using volume-preserving transformations
X. Yang, Y. Wang, J. Sun, X. Zhang, S. Zhang, Z. Li, and J. Yan · 2022
Closest in time.
On the identifiability of nonlinear ICA: sparsity and beyond
Y. Zheng, I. Ng, and K. Zhang · 2022
Closest in time.