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In this paper, we present a novel and principled approach to learn the optimal transport between two distributions, from samples.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
Differential equations methods for the Monge-Kantorovich mass transfer problem , volume 653
Evans, L. C. and Gangbo, W · 1999
Earlier work this paper cites.
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Earlier work this paper cites.
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Villani, C · 2003
Earlier work this paper cites.
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Chartrand, R., Wohlberg, B., Vixie, K., and Bollt, E · 2009
Earlier work this paper cites.
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Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., and Vaughan, J. W · 2010
Earlier work this paper cites.
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Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
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