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Generative Adversarial Networks (GANs) have achieved a great success in unsupervised learning.
J. Moser, “On the volume elements on a manifold,” Transactions of the American Mathematical Society
1965
Earlier work this paper cites.
R. M. Dudley, “The sizes of compact subsets of hilbert space and continuity of gaussian processes,” Journal of Functional Analysis
1967
Earlier work this paper cites.
J. I. Urbas, “Regularity of generalized solutions of monge-ampere equations,” Mathematische Zeitschrift
1988
Earlier work this paper cites.
G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Mathematics of control, signals and systems
1989
Earlier work this paper cites.
K. Hornik, “Approximation capabilities of multilayer feedforward networks,” Neural networks
1991
Earlier work this paper cites.
L. A. Caffarelli, “The regularity of mappings with a convex potential,” Journal of the American Mathematical Society
1992
Earlier work this paper cites.
L. A. Caffarelli, “Boundary regularity of maps with convex potentials,” Communications on pure and applied mathematics
1992
Earlier work this paper cites.
C. K. Chui and X. Li, “Approximation by ridge functions and neural networks with one hidden layer,” Journal of Approximation Theory
1992
Earlier work this paper cites.
A. R. Barron, “Universal approximation bounds for superpositions of a sigmoidal function,” IEEE Transactions on Information theory
1993
Earlier work this paper cites.
L. A. Caffarelli, “Boundary regularity of maps with convex potentials–ii,” Annals of mathematics
1996
Earlier work this paper cites.
H. N. Mhaskar, “Neural networks for optimal approximation of smooth and analytic functions,” Neural computation
1996
Earlier work this paper cites.
A. W. Van Der Vaart and J. A. Wellner, “Weak convergence,” in Weak convergence and empirical processes
1996
Earlier work this paper cites.
A. Müller, “Integral probability metrics and their generating classes of functions,” Advances in Applied Probability
1997
Earlier work this paper cites.
J. Urbas, “On the second boundary value problem for equations of monge-ampere type,” Journal fur die Reine und Angewandte Mathematik
1997
Earlier work this paper cites.
J. B. Tenenbaum, V. De Silva, and J. C. Langford, “A global geometric framework for nonlinear dimensionality reduction,” Science
2000
Earlier work this paper cites.
S. T. Roweis and L. K. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” science
2000
Earlier work this paper cites.
T.-W. Lee and M. S. Lewicki, “Unsupervised image classification, segmentation, and enhancement using ica mixture models,” IEEE Transactions on Image Processing
2002
Earlier work this paper cites.
New York, NY, USA: Springer Science & Business Media, 2006
L. Wasserman, All of nonparametric statistics · 2006
Earlier work this paper cites.
New York, NY, USA: Springer Science & Business Media, 2006
L. Györfi, M. Kohler, A. Krzyzak, and H. Walk, A distribution-free theory of nonparametric regression · 2006
Earlier work this paper cites.
R. Nickl and B. M. Pötscher, “Bracketing metric entropy rates and empirical central limit theorems for function classes of besov-and sobolev-type,” Journal of Theoretical Probability
2007
Earlier work this paper cites.
New York, NY, USA: Springer Science & Business Media, 2008
C. Villani, Optimal transport: old and new · 2008
Earlier work this paper cites.
New York, NY, USA: Springer Science & Business Media, 2008
A. B. Tsybakov, Introduction to nonparametric estimation · 2008
Earlier work this paper cites.
V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th international conference on machine learning (ICML-10)
2010
Earlier work this paper cites.
N. Chen, J. Zhu, and E. Xing, “Predictive subspace learning for multi-view data: a large margin approach,” Advances in neural information processing systems
2010
Earlier work this paper cites.
2010
Earlier work this paper cites.
X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems
2012
Earlier work this paper cites.
A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in ICML Workshop on Deep Learning for Audio, Speech, and Language Processing
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems
2014
Cited alongside, same era.
2014
Cited alongside, same era.
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in European conference on computer vision
2014
Cited alongside, same era.
2015
Cited alongside, same era.
Cambridge, MA, USA: MIT press, 2018
M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of machine learning · 2018
Later among the works it cites.
B. Zhou, D. Bau, A. Oliva, and A. Torralba, “Interpreting deep visual representations via network dissection,” IEEE transactions on pattern analysis and machine intelligence
2018
Later among the works it cites.
2018
Later among the works it cites.
A. Jacot, F. Gabriel, and C. Hongler, “Neural tangent kernel: Convergence and generalization in neural networks,” in Advances in neural information processing systems
2018
Later among the works it cites.
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2016
Cited alongside, same era.
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training gans,” in Advances in neural information processing systems
2016
Cited alongside, same era.
Cambridge, MA, USA: MIT Press, 2016
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning · 2016
Cited alongside, same era.
N. Courty, R. Flamary, D. Tuia, and A. Rakotomamonjy, “Optimal transport for domain adaptation,” IEEE transactions on pattern analysis and machine intelligence
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al
2017
Cited alongside, same era.
K. Schawinski, C. Zhang, H. Zhang, L. Fowler, and G. K. Santhanam, “Generative adversarial networks recover features in astrophysical images of galaxies beyond the deconvolution limit,” Monthly Notices of the Royal Astronomical Society: Letters
2017
Cited alongside, same era.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
Y. Li and Y. Liang, “Learning overparameterized neural networks via stochastic gradient descent on structured data,” in Advances in Neural Information Processing Systems
2018
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
Z. Allen-Zhu, Y. Li, and Y. Liang, “Learning and generalization in overparameterized neural networks, going beyond two layers,” in Advances in neural information processing systems
2019
Later among the works it cites.
2019
Later among the works it cites.
M. Chen, H. Jiang, W. Liao, and T. Zhao, “Efficient approximation of deep relu networks for functions on low dimensional manifolds,” in Advances in Neural Information Processing Systems
2019
Later among the works it cites.
J. Weed and F. Bach, “Sharp asymptotic and finite-sample rates of convergence of empirical measures in wasserstein distance,” Bernoulli
2019
Later among the works it cites.
Cambridge University Press, 2019
M. J. Wainwright, High-dimensional statistics: A non-asymptotic viewpoint · 2019
Later among the works it cites.
2020
Closest in time.
2020
Closest in time.
Y. Lu and J. Lu, “A universal approximation theorem of deep neural networks for expressing probability distributions,” Advances in neural information processing systems
2020
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2020
Closest in time.
N. Schreuder, V.-E. Brunel, and A. Dalalyan, “Statistical guarantees for generative models without domination,” in Algorithmic Learning Theory
2021
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2021
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2021
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P. Pauli, A. Koch, J. Berberich, P. Kohler, and F. Allgöwer, “Training robust neural networks using lipschitz bounds,” IEEE Control Systems Letters
2021
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H. Gouk, E. Frank, B. Pfahringer, and M. J. Cree, “Regularisation of neural networks by enforcing lipschitz continuity,” Machine Learning
2021
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2021
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2022
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2022
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J. Huang, Y. Jiao, Z. Li, S. Liu, Y. Wang, and Y. Yang, “An error analysis of generative adversarial networks for learning distributions,” Journal of Machine Learning Research
2022
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