Log in

Relevant bibliographies by topics / Distance de Wasserstein

Contents

Journal articles

Academic literature on the topic 'Distance de Wasserstein'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 June 2024

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Distance de Wasserstein.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Distance de Wasserstein"

1

Vayer, Titouan, Laetitia Chapel, Remi Flamary, Romain Tavenard, and Nicolas Courty. "Fused Gromov-Wasserstein Distance for Structured Objects." Algorithms 13, no. 9 (2020): 212. http://dx.doi.org/10.3390/a13090212.

Full text

Abstract:

Optimal transport theory has recently found many applications in machine learning thanks to its capacity to meaningfully compare various machine learning objects that are viewed as distributions. The Kantorovitch formulation, leading to the Wasserstein distance, focuses on the features of the elements of the objects, but treats them independently, whereas the Gromov–Wasserstein distance focuses on the relations between the elements, depicting the structure of the object, yet discarding its features. In this paper, we study the Fused Gromov-Wasserstein distance that extends the Wasserstein and

APA, Harvard, Vancouver, ISO, and other styles

2

Çelik, Türkü Özlüm, Asgar Jamneshan, Guido Montúfar, Bernd Sturmfels, and Lorenzo Venturello. "Wasserstein distance to independence models." Journal of Symbolic Computation 104 (May 2021): 855–73. http://dx.doi.org/10.1016/j.jsc.2020.10.005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Gangbo, Wilfrid, and Robert J. McCann. "Shape recognition via Wasserstein distance." Quarterly of Applied Mathematics 58, no. 4 (2000): 705–37. http://dx.doi.org/10.1090/qam/1788425.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Decreusefond, L. "Wasserstein Distance on Configuration Space." Potential Analysis 28, no. 3 (2008): 283–300. http://dx.doi.org/10.1007/s11118-008-9077-5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Mathey-Prevot, Maxime, and Alain Valette. "Wasserstein distance and metric trees." L’Enseignement Mathématique 69, no. 3 (2023): 315–33. http://dx.doi.org/10.4171/lem/1052.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Harmati, István Á., Lucian Coroianu, and Robert Fullér. "Wasserstein distance for OWA operators." Fuzzy Sets and Systems 484 (May 2024): 108931. http://dx.doi.org/10.1016/j.fss.2024.108931.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Peyre, Rémi. "Comparison between W2 distance and Ḣ−1 norm, and Localization of Wasserstein distance". ESAIM: Control, Optimisation and Calculus of Variations 24, № 4 (2018): 1489–501. http://dx.doi.org/10.1051/cocv/2017050.

Full text

Abstract:

It is well known that the quadratic Wasserstein distance W2(⋅, ⋅) is formally equivalent, for infinitesimally small perturbations, to some weighted H−1 homogeneous Sobolev norm. In this article I show that this equivalence can be integrated to get non-asymptotic comparison results between these distances. Then I give an application of these results to prove that the W2 distance exhibits some localization phenomenon: if μ and ν are measures on ℝn and ϕ: ℝn → ℝ+ is some bump function with compact support, then under mild hypotheses, you can bound above the Wasserstein distance between ϕ ⋅ μ and

APA, Harvard, Vancouver, ISO, and other styles

8

Xu, Minkai. "Towards Generalized Implementation of Wasserstein Distance in GANs." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (2021): 10514–22. http://dx.doi.org/10.1609/aaai.v35i12.17258.

Full text

Abstract:

Wasserstein GANs (WGANs), built upon the Kantorovich-Rubinstein (KR) duality of Wasserstein distance, is one of the most theoretically sound GAN models. However, in practice it does not always outperform other variants of GANs. This is mostly due to the imperfect implementation of the Lipschitz condition required by the KR duality. Extensive work has been done in the community with different implementations of the Lipschitz constraint, which, however, is still hard to satisfy the restriction perfectly in practice. In this paper, we argue that the strong Lipschitz constraint might be unnecessar

APA, Harvard, Vancouver, ISO, and other styles

9

Dou, Jason Xiaotian, Lei Luo, and Raymond Mingrui Yang. "An Optimal Transport Approach to Deep Metric Learning (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 12935–36. http://dx.doi.org/10.1609/aaai.v36i11.21604.

Full text

Abstract:

Capturing visual similarity among images is the core of many computer vision and pattern recognition tasks. This problem can be formulated in such a paradigm called metric learning. Most research in the area has been mainly focusing on improving the loss functions and similarity measures. However, due to the ignoring of geometric structure, existing methods often lead to sub-optimal results. Thus, several recent research methods took advantage of Wasserstein distance between batches of samples to characterize the spacial geometry. Although these approaches can achieve enhanced performance, the

APA, Harvard, Vancouver, ISO, and other styles

10

Tong, Qijun, and Kei Kobayashi. "Entropy-Regularized Optimal Transport on Multivariate Normal and q-normal Distributions." Entropy 23, no. 3 (2021): 302. http://dx.doi.org/10.3390/e23030302.

Full text

Abstract:

The distance and divergence of the probability measures play a central role in statistics, machine learning, and many other related fields. The Wasserstein distance has received much attention in recent years because of its distinctions from other distances or divergences. Although computing the Wasserstein distance is costly, entropy-regularized optimal transport was proposed to computationally efficiently approximate the Wasserstein distance. The purpose of this study is to understand the theoretical aspect of entropy-regularized optimal transport. In this paper, we focus on entropy-regulari

APA, Harvard, Vancouver, ISO, and other styles

More sources

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!