Entropic Approximation of Wasserstein Gradient Flows

By G. Peyr'e. 2015

141 citations recorded by the source corpus.

Subfields: Applied Mathematics, Computational Mechanics, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Mathematics

1. G. Peyr'e
2. Marco Cuturi
3. Lénaïc Chizat
4. Bernhard Schmitzer
5. Franccois-Xavier Vialard
6. Frank R. Schmidt
7. J. Kolter
8. Eric Wong

In Computational Mechanics

1. Marco Cuturi
2. Lénaïc Chizat
3. J. Kolter
4. Frank R. Schmidt
5. Eric Wong
6. Bernhard Schmitzer
7. Franccois-Xavier Vialard
8. G. Peyr'e

In Radiology, Nuclear Medicine and Imaging

1. G. Peyr'e
2. Marco Cuturi
3. Lénaïc Chizat
4. Bernhard Schmitzer
5. Franccois-Xavier Vialard
6. Frank R. Schmidt
7. J. Kolter
8. Eric Wong

Citations in the corpus

• Wasserstein Adversarial Examples via Projected Sinkhorn Iterations (2019, methodology): “Proximal operators with respect to Wasserstein instead of Euclidean distances are used in gradient flow problems (Jordan et al., 1998; Peyré, 2015), but are not relevant to our setting.”
• Computational Optimal Transport (2018, methodology): “9: Examples of gradient flows evolutions, with drift V and congestion terms (from Peyré [2015]), so that F (α) = ∫ X V (x)dα(x) + ι≤κ(ρα).”
• Scaling Algorithms for Unbalanced Transport Problems (2016, methodology): “The use of entropic regularization has been proposed recently in [58] and studied theoretically in [20].”