Results 11 to 20 of about 33,205 (174)

Quantum Wasserstein distance based on an optimization over separable states [PDF]

Quantum, 2023
We define the quantum Wasserstein distance such that the optimization of the coupling is carried out over bipartite separable states rather than bipartite quantum states in general, and examine its properties. Surprisingly, we find that the self-distance
Géza Tóth, József Pitrik
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Entropy-Regularized Optimal Transport on Multivariate Normal and q-normal Distributions

Entropy, 2021
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 ...
Qijun Tong, Kei Kobayashi
doaj   +1 more source

Earth mover’s distance as a measure of CP violation

Journal of High Energy Physics, 2023
We introduce a new unbinned two sample test statistic sensitive to CP violation utilizing the optimal transport plan associated with the Wasserstein (earth mover’s) distance.
Adam Davis, Tony Menzo, Ahmed Youssef, Jure Zupan   +3 more
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Multimedia Analysis and Fusion via Wasserstein Barycenter

International Journal of Networked and Distributed Computing (IJNDC), 2020
Optimal transport distance, otherwise known as Wasserstein distance, recently has attracted attention in music signal processing and machine learning as powerful discrepancy measures for probability distributions.
Cong Jin, Junhao Wang, Jin Wei, Lifeng Tan, Shouxun Liu, Wei Zhao, Shan Liu, Xin Lv   +7 more
doaj   +1 more source

Fused Gromov-Wasserstein Distance for Structured Objects

Algorithms, 2020
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.
Titouan Vayer, Laetitia Chapel, Remi Flamary, Romain Tavenard, Nicolas Courty   +4 more
doaj   +1 more source

Free complete Wasserstein algebras [PDF]

Logical Methods in Computer Science, 2018
We present an algebraic account of the Wasserstein distances $W_p$ on complete metric spaces, for $p \geq 1$. This is part of a program of a quantitative algebraic theory of effects in programming languages.
Radu Mardare, Prakash Panangaden, Gordon D. Plotkin   +2 more
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Generalized Wasserstein distance and its application to transport equations with source [PDF]

, 2012
In this article, we generalize the Wasserstein distance to measures with different masses. We study the properties of such distance. In particular, we show that it metrizes weak convergence for tight sequences.
A. Figalli, A. Tosin, B. Maury, B. Maury, B. Piccoli, B. Piccoli, B. Piccoli, Benedetto Piccoli, E. Cristiani, Francesco Rossi, G. Crippa, J. Evers, J.A. Carrillo, L. Ambrosio, L. Ambrosio, L.C. Evans, P. Gwiazda, P. Gwiazda   +17 more
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The “Unreasonable” Effectiveness of the Wasserstein Distance in Analyzing Key Performance Indicators of a Network of Stores

Big Data and Cognitive Computing, 2022
Large retail companies routinely gather huge amounts of customer data, which are to be analyzed at a low granularity. To enable this analysis, several Key Performance Indicators (KPIs), acquired for each customer through different channels are associated
Andrea Ponti, Ilaria Giordani, Matteo Mistri, Antonio Candelieri, Francesco Archetti   +4 more
doaj   +1 more source

Tanaka Theorem for Inelastic Maxwell Models [PDF]

, 2006
We show that the Euclidean Wasserstein distance is contractive for inelastic homogeneous Boltzmann kinetic equations in the Maxwellian approximation and its associated Kac-like caricature.
A. Pulvirenti, A.V. Bobylev, A.V. Bobylev, A.V. Bobylev, A.V. Bobylev, A.V. Bobylev, A.V. Bobylev, A.V. Bobylev, A.V. Bobylev, C. Cercignani, C. Villani, E. Caglioti, E. Gabetta, E.A. Carlen, F. Bolley, G. Toscani, H. Li, H. Tanaka, H. Tanaka, H.P. McKean Jr, J. A. Carrillo, J.A. Carrillo, J.A. Carrillo, M. Bisi, M. Bisi, M. Kac, M.H. Ernst, M.H. Ernst, S. Mischler, S. Mischler   +29 more
core   +4 more sources

Open-Set Signal Recognition Based on Transformer and Wasserstein Distance

Applied Sciences, 2023
Open-set signal recognition provides a new approach for verifying the robustness of models by introducing novel unknown signal classes into the model testing and breaking the conventional closed-set assumption, which has become very popular in real-world
Wei Zhang, Da Huang, Minghui Zhou, Jingran Lin, Xiangfeng Wang   +4 more
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