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Optimal Transport (OT) naturally arises in many machine learning applications, yet the heavy computational burden limits its wide-spread uses.
The monge–kantorovich mass transference problem and its stochastic applications
Rachev, S. T · 1985
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The gravity model in transportation analysis: theory and extensions
Erlander, S · 1990
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An isotropic 3 × \times 3 image gradient operater
Sobel, I · 1990
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A database for handwritten text recognition research
Hull, J. J · 1994
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Gradient-based learning applied to document recognition
LeCun, Y · 1998
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Eigenvalue computation in the 20th century
Golub, G. H · 2001
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Impulse functions over curves and surfaces and their applications to diffraction
Onural, L · 2006
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Methods of numerical integration
Davis, P. J · 2007
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Optimal transport: old and new
Villani, C · 2008
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Models and applications of optimal transport in economics, traffic and urban planning
Santambrogio, F · 2010
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Reading digits in natural images with unsupervised feature learning
Netzer, Y · 2011
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Optimal transportation and economic applications
Carlier, G · 2012
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Sinkhorn distances: Lightspeed computation of optimal transport
Cuturi, M · 2013
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Auto-encoding variational bayes
Kingma, D. P · 2013
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Rectifier nonlinearities improve neural network acoustic models
Maas, A. L · 2013
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Nice: Non-linear independent components estimation
Dinh, L · 2014
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Unsupervised domain adaptation by backpropagation
Ganin, Y · 2014
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Generative adversarial nets
Goodfellow, I · 2014
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Adam: A method for stochastic optimization
Kingma, D. P · 2014
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Iterative bregman projections for regularized transportation problems
Benamou, J.-D · 2015
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Unbalanced optimal transport: geometry and kantorovich formulation
Chizat, L · 2015
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Learning with a wasserstein loss
Frogner, C · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K · 2015
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Multi-marginal optimal transport: theory and applications
Pass, B · 2015
Calibrating energy-based generative adversarial networks
Dai, Z · 2017
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A survey of some recent applications of optimal transport methods to econometrics
Galichon, A · 2017
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Image-to-image translation with conditional adversarial networks
Isola, P · 2017
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Computational optimal transport
Peyré, G · 2017
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Large-scale optimal transport and mapping estimation
Seguy, V · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A · 2015
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Convolutional wasserstein distances: Efficient optimal transportation on geometric domains
Solomon, J · 2015
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Chen, X · 2016
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Density estimation using real nvp
Dinh, L · 2016
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Stochastic optimization for large-scale optimal transport
Genevay, A · 2016
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Recent progress and review of issues related to physics dynamics coupling in geophysical models
Gross, M · 2016
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Zhu, J.-Y · 2017
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Large scale gan training for high fidelity natural image synthesis
Brock, A · 2018
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Neural ordinary differential equations
Chen, T. Q · 2018
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Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation
Damodaran, B. B · 2018
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Ffjord: Free-form continuous dynamics for scalable reversible generative models
Grathwohl, W · 2018
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On computation and generalization of gans with spectrum control
Jiang, H · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P · 2018
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Multi-task adversarial network for disentangled feature learning
Liu, Y · 2018
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Spectral normalization for generative adversarial networks
Miyato, T · 2018
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Large scale optimal transport and mapping estimation
Seguy, V · 2018
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A fast proximal point method for wasserstein distance
Xie, Y · 2018
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Scalable unbalanced optimal transport using generative adversarial networks
Yang, K. D · 2018
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