Results 171 to 180 of about 3,027 (198)

M²S-YOLOv8: Multi-Scale and Asymmetry-Aware Ship Detection for Marine Environments. [PDF]

Sensors (Basel)
Li P, Qiao D, Mu J, Qi L.
europepmc   +1 more source

Feasibility study of unsupervised anomaly detection using Wasserstein GAN in SPECT image. [PDF]

EJNMMI Phys
Kasai R, Otsuka H.
europepmc   +1 more source

Interpretable metrics for evaluating fidelity, diversity, and privacy in synthetic EEG data

Silveira I, Silva L, Furk D, Marques B, Ao N, Hoogteijling S, Heijink I, Zijlmans M, Gamboa H.   +8 more
europepmc   +1 more source

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Why Wasserstein Metric Is Useful in Econometrics

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2023
In many practical situations, we need to change the spatial distribution of some goods. In such situations, it is desirable to minimize the overall transportation costs. In the 1-D case, the smallest transportation cost of such a change is proportional to what is known as the Wasserstein metric.
Nguyen Ngoc Thach, Nguyen Duc Trung, R. Noah Padilla   +2 more
openaire   +1 more source

Network Consensus in the Wasserstein Metric Space of Probability Measures [PDF]

SIAM Journal on Control and Optimization, 2021
A preliminary draft of this work appeared in a conference proceedings as: "A.N. Bishop and A. Doucet. Distributed nonlinear consensus in the space of probability measures. In Proc. of the 19th IFAC World Congress, Cape Town, South Africa, August 2014."
Arnaud Doucet, Adrian N Bishop
exaly   +3 more sources

The quadratic Wasserstein metric for earthquake location [PDF]

Journal of Computational Physics, 2018
25 pages, 13 ...
Yifan Chen, Dinghui Yang, Jing Chen
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{Euclidean, metric, and Wasserstein} gradient flows: an overview

Bulletin of Mathematical Sciences, 2017
This is an expository paper on the theory of gradient flows, and in particular of those PDEs which can be interpreted as gradient flows for the Wasserstein metric on the space of probability measures (a distance induced by optimal transport). The starting point is the Euclidean theory, and then its generalization to metric spaces, according to the work
Filippo Santambrogio, Santambrogio Filippo   +1 more
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Distributionally Robust Games: Wasserstein Metric

2018 International Joint Conference on Neural Networks (IJCNN), 2018
Deep generative models are powerful but difficult to train due to its instability, saturation problem and high dimensional data distribution. This paper introduces a game theory framework with Wasserstein metric to train generative models, in which the unknown data distribution is learned by dynamically optimizing the worst-case payoff.
Jian Gao 0006, Hamidou Tembine
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Gromov–Wasserstein Distances and the Metric Approach to Object Matching

Foundations of Computational Mathematics, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Facundo Memoli, Memoli Facundo
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Frequency domain model validation in Wasserstein metric

2013 American Control Conference, 2013
This paper connects the time-domain uncertainty propagation approach for model validation in Wasserstein distance 2W2, introduced by the authors in [1], with the frequency domain model validation in the same. To the best of our knowledge, this is the first frequency domain interpretation of Monge-Kantorovich optimal transport.
Abhishek Halder, Raktim Bhattacharya
openaire   +1 more source