Fetching the paper…
Reading the bibliography…
We propose a new formulation and learning strategy for computing the Wasserstein geodesic between two probability distributions in high dimensions.
Nonlinear programming
Kuhn, H. W. and Tucker, A. W · 1951
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
Color transfer between images
Reinhard, E., Ashikhmin, M., Gooch, B., and Shirley, P · 2001
Earlier work this paper cites.
How to train your neural ode: the world of jacobian and kinetic regularization
Finlay, C., Jacobsen, J.-H., Nurbekyan, L., and Oberman, A. M · 2002
Earlier work this paper cites.
Topics in optimal transportation
Villani, C · 2003
Earlier work this paper cites.
Learning normalizing flows from entropy-kantorovich potentials
Finlay, C., Gerolin, A., Oberman, A., and Pooladian, A · 2006
Earlier work this paper cites.
Optimal transport: old and new , volume 338
Villani, C · 2008
Earlier work this paper cites.
Two numerical methods for the elliptic monge-ampere equation
Benamou, J., Froese, B., and Oberman, A · 2010
Earlier work this paper cites.
Sinkhorn distances: Lightspeed computation of optimal transport
Cuturi, M · 2013
Earlier work this paper cites.
Optimal transport with laplacian regularization
Flamary, R., Courty, N., Rakotomamonjy, A., and Tuia, D · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization, 2014
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
On the relation between optimal transport and schrödinger bridges: A stochastic control viewpoint
Chen, Y., Georgiou, T., and Pavon, M · 2016
Earlier work this paper cites.
Optimal transport for domain adaptation
Courty, N., Flamary, R., Tuia, D., and Rakotomamonjy, A · 2016
Earlier work this paper cites.
Stochastic optimization for large-scale optimal transport
Genevay, A., Cuturi, M., Peyré, G., and Bach, F · 2016
Earlier work this paper cites.
Data‐driven optimal transport
Trigila, G. and Tabak, E · 2016
Earlier work this paper cites.
Near-linear time approximation algorithms for optimal transport via sinkhorn iteration
Altschuler, J., Niles-Weed, J., and Rigollet, P · 2017
Earlier work this paper cites.
Sinkhorn distances: Lightspeed computation of optimal transport
Amos, B., Xu, L., and Kolter, J · 2017
Cited alongside, same era.
Arjovsky, M., Chintala, S., and Bottou, L · 2017
Cited alongside, same era.
Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C · 2017
Cited alongside, same era.
Preconditioning of optimal transport
Kuang, M. and Tabak, E. G · 2017
Cited alongside, same era.
Tsallis regularized optimal transport and ecological inference
Muzellec, B., Nock, R., Patrini, G., and Nielsen, F · 2017
Cited alongside, same era.
Wasserstein-2 generative networks
Korotin, A., Egiazarian, V., Asadulaev, A., Safin, A., and Burnaev, E · 2019
Later among the works it cites.
Sample‐based optimal transport and barycenter problems
Kuang, M. and Tabak, E · 2019
Later among the works it cites.
Learning to match via inverse optimal transport
Li, R., Ye, X., Zhou, H., and Zha, H · 2019
Later among the works it cites.
On scalable and efficient computation of large scale optimal transport
Xie, Y., Chen, M., Jiang, H., Zhao, T., and Zha, H · 2019
Later among the works it cites.
Potential flow generator with l 2 l_{2} optimal transport regularity for generative models
Yang, L. and Karniadakis, G · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Seguy, V., Damodaran, B., Flamary, R., Courty, N., R., A., and Blondel, M · 2017
Cited alongside, same era.
Tolstikhin, I., Bousquet, O., Gelly, S., and Schoelkopf, B · 2017
Cited alongside, same era.
Regularized optimal transport and the rot mover’s distance
Dessein, A., Papadakis, N., and Rouas, J · 2018
Cited alongside, same era.
Learning generative models with sinkhorn divergences
Genevay, A., Peyré, G., and Cuturi, M · 2018
Cited alongside, same era.
Ffjord: Free-form continuous dynamics for scalable reversible generative models
Grathwohl, W., Chen, R., Bettencourt, J., Sutskever, I., and Duvenaud, D · 2018
Cited alongside, same era.
Distributed optimal transport for the deployment of swarms
Krishnan, V. and Martínez, S · 2018
Cited alongside, same era.
A parallel method for earth mover’s distance
Li, W., Ryu, E., Osher, S., Yin, W., and Gangbo, W · 2018
Cited alongside, same era.
Chen, Y., Georgiou, T., and Pavon, M · 2020
Later among the works it cites.
Scalable computations of wasserstein barycenter via input convex neural networks
Fan, J., Taghvaei, A., and Chen, Y · 2020
Later among the works it cites.
Optimal transport-based coverage control for swarm robot systems: Generalization of the voronoi tessellation-based method
Inoue, D., Ito, Y., and Yoshida, H · 2020
Later among the works it cites.
Lin, A., Fung, S., Li, W., Nurbekyan, L., and Osher, S · 2020
Later among the works it cites.
Learning stochastic behaviour of aggregate data
Ma, S., Liu, S., Zha, H., and Zhou, H · 2020
Later among the works it cites.
Optimal transport mapping via input convex neural networks
Makkuva, A.and Taghvaei, A., Oh, S., and Lee, J · 2020
Later among the works it cites.
A machine learning framework for solving high-dimensional mean field game and mean field control problems
Ruthotto, L., Osher, S. J., Li, W., Nurbekyan, L., and Fung, S. W · 2020
Later among the works it cites.
A fast proximal point method for computing exact wasserstein distance
Xie, Y., Wang, X., Wang, R., and Zha, H · 2020
Later among the works it cites.
Wasserstein distributionally robust stochastic control: A data-driven approach
Yang, I · 2020
Later among the works it cites.
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., Gautheron, L., Gayraud, N. T., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D. J., Tavenard, R., Tong, A., and Vayer, T · 2021
Closest in time.