The Monge Gap: A Regularizer to Learn All Transport Maps

9 February 2023

Papers citing "The Monge Gap: A Regularizer to Learn All Transport Maps"

10 / 10 papers shown

Title
Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold Lazar Atanackovic Xi Zhang Brandon Amos Mathieu Blanchette Leo J. Lee Yoshua Bengio Alexander Tong Kirill Neklyudov 31 5 0 26 Aug 2024
On amortizing convex conjugates for optimal transport Brandon Amos OT 70 27 0 21 Oct 2022
Turning Normalizing Flows into Monge Maps with Geodesic Gaussian Preserving Flows G. Morel Lucas Drumetz Simon Benaïchouche Nicolas Courty F. Rousseau OT 26 6 0 22 Sep 2022
Neural Optimal Transport with General Cost Functionals Arip Asadulaev Alexander Korotin Vage Egiazarian Petr Mokrov Evgeny Burnaev OT 78 32 0 30 May 2022
An Optimal Transport Perspective on Unpaired Image Super-Resolution Milena Gazdieva Litu Rout Alexander Korotin A. Kravchenko Alexander N. Filippov Evgeny Burnaev OT 37 11 0 02 Feb 2022
Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein Marco Cuturi Laetitia Meng-Papaxanthos Yingtao Tian Charlotte Bunne Geoff Davis O. Teboul OT 143 94 0 28 Jan 2022
Neural Optimal Transport Alexander Korotin Daniil Selikhanovych Evgeny Burnaev OT 124 88 0 28 Jan 2022
Entropic estimation of optimal transport maps Aram-Alexandre Pooladian Jonathan Niles-Weed OT 74 105 0 24 Sep 2021
Wasserstein-2 Generative Networks Alexander Korotin Vage Egiazarian Arip Asadulaev Alexander Safin E. Burnaev GAN 115 100 0 28 Sep 2019
Input Convex Neural Networks Brandon Amos Lei Xu J. Zico Kolter 173 596 0 22 Sep 2016