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While the optimal transport (OT) problem was originally formulated as a linear program, the addition of entropic regularization has proven beneficial both computationally and statistically, for many applications.
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Scikit-learn: Machine learning in Python
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The mnist database of handwritten digit images for machine learning research
Deng, L. (2012) · 2012
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Sinkhorn distances: Lightspeed computation of optimal transport
Cuturi, M. (2013) · 2013
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Domain adaptation with regularized optimal transport
Courty, N., Flamary, R., and Tuia, D. (2014) · 2014
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Semi-supervised learning with deep generative models
Kingma, D. P., Mohamed, S., Jimenez Rezende, D., and Welling, M. (2014) · 2014
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Glove: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C. D. (2014) · 2014
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A smoothed dual approach for variational wasserstein problems
Cuturi, M. and Peyré, G. (2015) · 2015
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From word embeddings to document distances
Kusner, M., Sun, Y., Kolkin, N., and Weinberger, K. (2015) · 2015
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Optimal transport for applied mathematicians
Santambrogio, F. (2015) · 2015
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Convolutional Wasserstein distances: efficient optimal transportation on geometric domains
Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A., Du, T., and Guibas, L. (2015) · 2015
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Learning Population-Level Diffusions with Generative Recurrent Networks
Hashimoto, T., Gifford, D., and Jaakkola, T. (2016) · 2016
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Multi-to one-dimensional optimal transport
Chiappori, P.-A., McCann, R. J., and Pass, B. (2017) · 2017
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Joint distribution optimal transportation for domain adaptation
Courty, N., Flamary, R., Habrard, A., and Rakotomamonjy, A. (2017) · 2017
Unsupervised learning of visual features by contrasting cluster assignments
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., and Joulin, A. (2020) · 2020
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Faster wasserstein distance estimation with the sinkhorn divergence
Chizat, L., Roussillon, P., Léger, F., Vialard, F.-X., and Peyré, G. (2020) · 2020
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Supervised quantile normalization for low rank matrix factorization
Cuturi, M., Teboul, O., Niles-Weed, J., and Vert, J.-P. (2020) · 2020
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Geometric data analysis, beyond convolutions
Feydy, J. (2020) · 2020
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Multi-subject meg/eeg source imaging with sparse multi-task regression
Janati, H., Bazeille, T., Thirion, B., Cuturi, M., and Gramfort, A. (2020) · 2020
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Superglue: Learning feature matching with graph neural networks
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Jax: composable transformations of python+ numpy programs
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., et al. (2018) · 2018
Cited alongside, same era.
Optimal transport for gaussian mixture models
Chen, Y., Georgiou, T. T., and Tannenbaum, A. (2018) · 2018
Cited alongside, same era.
Learning generative models with sinkhorn divergences
Genevay, A., Peyré, G., and Cuturi, M. (2018) · 2018
Cited alongside, same era.
Differential properties of sinkhorn approximation for learning with wasserstein distance
Luise, G., Rudi, A., Pontil, M., and Ciliberto, C. (2018) · 2018
Cited alongside, same era.
Improving GANs using optimal transport
Salimans, T., Zhang, H., Radford, A., and Metaxas, D. (2018) · 2018
Cited alongside, same era.
Wasserstein dictionary learning: Optimal transport-based unsupervised nonlinear dictionary learning
Schmitz, M. A., Heitz, M., Bonneel, N., Ngole, F., Coeurjolly, D., Cuturi, M., Peyré, G., and Starck, J.-L. (2018) · 2018
Cited alongside, same era.
Sarlin, P.-E., DeTone, D., Malisiewicz, T., and Rabinovich, A. (2020) · 2020
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Linear time sinkhorn divergences using positive features
Scetbon, M. and Cuturi, M. (2020) · 2020
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Differentiable particle filtering via entropy-regularized optimal transport
Corenflos, A., Thornton, J., Deligiannidis, G., and Doucet, A. (2021) · 2021
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Acceleration methods
d’Aspremont, A., Scieur, D., Taylor, A., et al. (2021) · 2021
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Pot: Python optimal transport
Flamary, R., Courty, N., Gramfort, A., Alaya, M. Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., et al. (2021) · 2021
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A note on overrelaxation in the sinkhorn algorithm
Lehmann, T., Von Renesse, M.-K., Sambale, A., and Uschmajew, A. (2021) · 2021
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Entropic estimation of optimal transport maps
Pooladian, A.-A. and Niles-Weed, J. (2021) · 2021
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Overrelaxed sinkhorn–knopp algorithm for regularized optimal transport
Thibault, A., Chizat, L., Dossal, C., and Papadakis, N. (2021) · 2021
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Meta optimal transport
Amos, B., Cohen, S., Luise, G., and Redko, I. (2022) · 2022
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Optimal transport tools (ott): A jax toolbox for all things wasserstein
Cuturi, M., Meng-Papaxanthos, L., Tian, Y., Bunne, C., Davis, G., and Teboul, O. (2022) · 2022
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Sinkformers: Transformers with doubly stochastic attention
Sander, M. E., Ablin, P., Blondel, M., and Peyré, G. (2022) · 2022
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Faster unbalanced optimal transport: Translation invariant sinkhorn and 1-d frank-wolfe
Sejourne, T., Vialard, F.-X., and Peyré, G. (2022) · 2022
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