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Wasserstein distance plays increasingly important roles in machine learning, stochastic programming and image processing.
On mass transfer problem
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Color transfer between images
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A unified framework for some inexact proximal point algorithms
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On minimum kantorovich distance estimators
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Color transfer in correlated color space
Xiao, X · 2006
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Fast and robust earth mover’s distances
Pele, O · 2009
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Wasserstein barycenter and its application to texture mixing
Rabin, J · 2011
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Convergence rates of inexact proximal-gradient methods for convex optimization
Schmidt, M · 2011
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Sinkhorn distances: Lightspeed computation of optimal transport
Cuturi, M · 2013
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Fast computation of Wasserstein barycenters
Cuturi, M · 2014
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Generative adversarial nets
Stabilized sparse scaling algorithms for entropy regularized transport problems
Schmitzer, B · 2016
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Wasserstein generative adversarial networks
Arjovsky, M · 2017
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Dvurechensky, P · 2017
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POT Python Optimal Transport Library
Flamary, R · 2017
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Sinkhorn-AutoDiff: Tractable Wasserstein learning of generative models
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Goodfellow, I · 2014
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Proximal algorithms
Parikh, N · 2014
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Adaptive color transfer with relaxed optimal transport
Rabin, J · 2014
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Wasserstein propagation for semi-supervised learning
Solomon, J · 2014
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Iterative Bregman projections for regularized transportation problems
Benamou, J.-D · 2015
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Convolutional wasserstein distances: Efficient optimal transportation on geometric domains
Solomon, J · 2015
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Mandad, M · 2017
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Non-asymptotic convergence analysis of inexact gradient methods for machine learning without strong convexity
So, A. M.-C · 2017
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Parallel streaming Wasserstein barycenters
Staib, M · 2017
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Overrelaxed Sinkhorn-Knopp algorithm for regularized optimal transport
Thibault, A · 2017
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A transportation L p {L}^{p} distance for signal analysis
Thorpe, M · 2017
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Fast discrete distribution clustering using Wasserstein barycenter with sparse support
Ye, J · 2017
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Scaling algorithms for unbalanced transport problems
Chizat, L · 2018
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Stochastic wasserstein barycenters
Claici, S · 2018
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