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While theoretically appealing, the application of the Wasserstein distance to large-scale machine learning problems has been hampered by its prohibitive computational cost.
Uber die bestimmung von funktionen durch ihre integralwerte laengs gewisser mannigfaltigkeiten
J. Radon · 1917
Earlier work this paper cites.
The Radon transform , volume 2
S. Helgason · 1980
Earlier work this paper cites.
The inversion problem and applications of the generalized Radon transform
G. Beylkin · 1984
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
The universality of the Radon transform , chapter 5, pages 299–363
L. Ehrenpreis · 2003
Earlier work this paper cites.
Optimal Transport: old and new , volume 338
C. Villani · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
Earlier work this paper cites.
Unidimensional and evolution methods for optimal transportation
N. Bonnotte · 2013
Earlier work this paper cites.
Sinkhorn distances: Lightspeed computation of optimal transport
M. Cuturi · 2013
Earlier work this paper cites.
Fast computation of Wasserstein barycenters
M. Cuturi and A. Doucet · 2014
Earlier work this paper cites.
Regularized discrete optimal transport
S. Ferradans, N. Papadakis, G. Peyré, and J. Aujol · 2014
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, A. J. Pouget, M. Mirza, B. Xu, F. D. Warde, et al · 2014
Earlier work this paper cites.
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M. Arjovsky, S. Chintala, and L. Bottou · 2017
Cited alongside, same era.
Improving GANs using optimal transport
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Later among the works it cites.
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J. Behrmann, W. Grathwohl, R. T. Q. Chen, D. Duvenaud, and J. Jacobsen · 2019
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On parameter estimation with the wasserstein distance
E. Bernton, P. E. Jacob, M. Gerber, and C. P. Robert · 2019
Later among the works it cites.
Max-sliced Wasserstein distance and its use for GANs
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Later among the works it cites.
Sample complexity of Sinkhorn divergences
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Later among the works it cites.
Invertible convolutional flow
M. Karami, D. Schuurmans, J. Sohl-Dickstein, L. Dinh, and D. Duckworth · 2019
Later among the works it cites.
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