Relevant bibliographies by topics / Entropic optimal transport

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Entropic optimal transport'

Author: Grafiati
Published: 7 December 2024
Last updated: 31 July 2025

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Journal articles on the topic "Entropic optimal transport"

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Altschuler, Jason M., Jonathan Niles-Weed, and Austin J. Stromme. "Asymptotics for Semidiscrete Entropic Optimal Transport." SIAM Journal on Mathematical Analysis 54, no. 2 (2022): 1718–41. http://dx.doi.org/10.1137/21m1440165.

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Keriven, Nicolas. "Entropic Optimal Transport on Random Graphs." SIAM Journal on Mathematics of Data Science 5, no. 4 (2023): 1028–50. http://dx.doi.org/10.1137/22m1518281.

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Amari, Shun-ichi, Ryo Karakida, Masafumi Oizumi, and Marco Cuturi. "Information Geometry for Regularized Optimal Transport and Barycenters of Patterns." Neural Computation 31, no. 5 (2019): 827–48. http://dx.doi.org/10.1162/neco_a_01178.

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We propose a new divergence on the manifold of probability distributions, building on the entropic regularization of optimal transportation problems. As Cuturi ( 2013 ) showed, regularizing the optimal transport problem with an entropic term is known to bring several computational benefits. However, because of that regularization, the resulting approximation of the optimal transport cost does not define a proper distance or divergence between probability distributions. We recently tried to introduce a family of divergences connecting the Wasserstein distance and the Kullback-Leibler divergence
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Rigollet, Philippe, and Jonathan Weed. "Entropic optimal transport is maximum-likelihood deconvolution." Comptes Rendus Mathematique 356, no. 11-12 (2018): 1228–35. http://dx.doi.org/10.1016/j.crma.2018.10.010.

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Clason, Christian, Dirk A. Lorenz, Hinrich Mahler, and Benedikt Wirth. "Entropic regularization of continuous optimal transport problems." Journal of Mathematical Analysis and Applications 494, no. 1 (2021): 124432. http://dx.doi.org/10.1016/j.jmaa.2020.124432.

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Junge, Oliver, Daniel Matthes, and Bernhard Schmitzer. "Entropic transfer operators." Nonlinearity 37, no. 6 (2024): 065004. http://dx.doi.org/10.1088/1361-6544/ad247a.

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Abstract We propose a new concept for the regularization and discretization of transfer and Koopman operators in dynamical systems. Our approach is based on the entropically regularized optimal transport between two probability measures. In particular, we use optimal transport plans in order to construct a finite-dimensional approximation of some transfer or Koopman operator which can be analyzed computationally. We prove that the spectrum of the discretized operator converges to the one of the regularized original operator, give a detailed analysis of the relation between the discretized and
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Amid, Ehsan, Frank Nielsen, Richard Nock, and Manfred K. Warmuth. "Optimal Transport with Tempered Exponential Measures." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (2024): 10838–46. http://dx.doi.org/10.1609/aaai.v38i10.28957.

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In the field of optimal transport, two prominent subfields face each other: (i) unregularized optimal transport, ``a-la-Kantorovich'', which leads to extremely sparse plans but with algorithms that scale poorly, and (ii) entropic-regularized optimal transport, ``a-la-Sinkhorn-Cuturi'', which gets near-linear approximation algorithms but leads to maximally un-sparse plans. In this paper, we show that an extension of the latter to tempered exponential measures, a generalization of exponential families with indirect measure normalization, gets to a very convenient middle ground, with both very fa
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PEYRÉ, GABRIEL, LÉNAÏC CHIZAT, FRANÇOIS-XAVIER VIALARD, and JUSTIN SOLOMON. "Quantum entropic regularization of matrix-valued optimal transport." European Journal of Applied Mathematics 30, no. 6 (2017): 1079–102. http://dx.doi.org/10.1017/s0956792517000274.

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This article introduces a new notion of optimal transport (OT) between tensor fields, which are measures whose values are positive semidefinite (PSD) matrices. This “quantum” formulation of optimal transport (Q-OT) corresponds to a relaxed version of the classical Kantorovich transport problem, where the fidelity between the input PSD-valued measures is captured using the geometry of the Von-Neumann quantum entropy. We propose a quantum-entropic regularization of the resulting convex optimization problem, which can be solved efficiently using an iterative scaling algorithm. This method is a ge
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Ito, Kaito, and Kenji Kashima. "Entropic model predictive optimal transport over dynamical systems." Automatica 152 (June 2023): 110980. http://dx.doi.org/10.1016/j.automatica.2023.110980.

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Lo, Chi-Jen (Roger), Mahesh K. Marina, Nishanth Sastry, Kai Xu, Saeed Fadaei, and Yong Li. "Shrinking VOD Traffic via Rényi-Entropic Optimal Transport." Proceedings of the ACM on Measurement and Analysis of Computing Systems 8, no. 1 (2024): 1–34. http://dx.doi.org/10.1145/3639033.

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In response to the exponential surge in Internet Video on Demand (VOD) traffic, numerous research endeavors have concentrated on optimizing and enhancing infrastructure efficiency. In contrast, this paper explores whether users' demand patterns can be shaped to reduce the pressure on infrastructure. Our main idea is to design a mechanism that alters the distribution of user requests to another distribution which is much more cache-efficient, but still remains 'close enough' (in the sense of cost) to fulfil each individual user's preference. To quantify the cache footprint of VOD traffic, we pr
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Dissertations / Theses on the topic "Entropic optimal transport"

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DE, PONTI NICOLÒ. "Optimal transport: entropic regularizations, geometry and diffusion PDEs." Doctoral thesis, Università degli studi di Pavia, 2019. http://hdl.handle.net/11571/1292130.

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Thurin, Gauthier. "Quantiles multivariés et transport optimal régularisé." Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0262.

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L’objet d’intérêt principal de cette thèse est la fonction quantile de Monge- Kantorovich. On s’intéresse d’abord à la question cruciale de son estimation, qui revient à résoudre un problème de transport optimal. En particulier, on tente de tirer profit de la connaissance a priori de la loi de référence, une information additionnelle par rapport aux algorithmes usuels, qui nous permet de paramétrer les potentiels de transport par leur série de Fourier. Ce faisant, la régularisation entropique du transport optimal permet deux avantages : la construction d’un algorithme efficace et convergent po
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Nenna, Luca. "Numerical Methods for Multi-Marginal Optimal Transportation." Thesis, Paris Sciences et Lettres (ComUE), 2016. http://www.theses.fr/2016PSLED017/document.

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Dans cette thèse, notre but est de donner un cadre numérique général pour approcher les solutions des problèmes du transport optimal (TO). L’idée générale est d’introduire une régularisation entropique du problème initial. Le problème régularisé correspond à minimiser une entropie relative par rapport à une mesure de référence donnée. En effet, cela équivaut à trouver la projection d’un couplage par rapport à la divergence de Kullback-Leibler. Cela nous permet d’utiliser l’algorithme de Bregman/Dykstra et de résoudre plusieurs problèmes variationnels liés au TO. Nous nous intéressons particuli
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Tamanini, Luca. "Analysis and Geometry of RCD spaces via the Schrödinger problem." Thesis, Paris 10, 2017. http://www.theses.fr/2017PA100082/document.

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Le but principal de ce manuscrit est celui de présenter une nouvelle méthode d'interpolation entre des probabilités inspirée du problème de Schrödinger, problème de minimisation entropique ayant des liens très forts avec le transport optimal. À l'aide de solutions au problème de Schrödinger, nous obtenons un schéma d'approximation robuste jusqu'au deuxième ordre et différent de Brenier-McCann qui permet d'établir la formule de dérivation du deuxième ordre le long des géodésiques Wasserstein dans le cadre de espaces RCD* de dimension finie. Cette formule était inconnue même dans le cadre des es
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Genevay, Aude. "Entropy-regularized Optimal Transport for Machine Learning." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLED002/document.

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Le Transport Optimal régularisé par l’Entropie (TOE) permet de définir les Divergences de Sinkhorn (DS), une nouvelle classe de distance entre mesures de probabilités basées sur le TOE. Celles-ci permettentd’interpolerentredeuxautresdistancesconnues: leTransport Optimal(TO)etl’EcartMoyenMaximal(EMM).LesDSpeuventêtre utilisées pour apprendre des modèles probabilistes avec de meilleures performances que les algorithmes existants pour une régularisation adéquate. Ceci est justifié par un théorème sur l’approximation des SDpardeséchantillons, prouvantqu’unerégularisationsusantepermet de se débarrass
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Baradat, Aymeric. "Transport optimal incompressible : dépendance aux données et régularisation entropique." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLX016/document.

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Cette thèse porte sur le problème de transport optimal incompressible, un problème introduit par Brenier à la fin des années 80 dans le but de décrire l’évolution d’un fluide incompressible et non-visqueux de façon lagrangienne, ou autrement dit en fixant l’état initial et l’état final de ce fluide, et en minimisant une certaine fonctionnelle sur un ensemble de dynamiques admissibles. Ce manuscrit contient deux parties.Dans la première, on étudie la dépendance du problème de transport optimal incompressible par rapport aux données. Plus précisément, on étudie la dépendance du champ de pression
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Ripani, Luigia. "Le problème de Schrödinger et ses liens avec le transport optimal et les inégalités fonctionnelles." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE1274/document.

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Au cours des 20 dernières années, la théorie du transport optimal s’est revelée être un outil efficace pour étudier le comportement asymptotique dans le cas des équations de diffusion, pour prouver des inégalités fonctionnelles et pour étendre des propriétés géométriques dans des espaces extrêmement généraux comme des espaces métriques mesurés, etc. La condition de courbure-dimension de la théorie Bakry-Emery apparaît comme la pierre angulaire de ces applications. Il suffit de penser au cas le plus simple et le plus important de la distance quadratique de Wasserstein W2 : la contraction du flu
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Chizat, Lénaïc. "Transport optimal de mesures positives : modèles, méthodes numériques, applications." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLED063/document.

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L'objet de cette thèse est d'étendre le cadre théorique et les méthodes numériques du transport optimal à des objets plus généraux que des mesures de probabilité. En premier lieu, nous définissons des modèles de transport optimal entre mesures positives suivant deux approches, interpolation et couplage de mesures, dont nous montrons l'équivalence. De ces modèles découle une généralisation des métriques de Wasserstein. Dans une seconde partie, nous développons des méthodes numériques pour résoudre les deux formulations et étudions en particulier une nouvelle famille d'algorithmes de "scaling",
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DOLDI, ALESSANDRO. "EQUILIBRIUM, SYSTEMIC RISK MEASURES AND OPTIMAL TRANSPORT: A CONVEX DUALITY APPROACH." Doctoral thesis, Università degli Studi di Milano, 2021. http://hdl.handle.net/2434/812668.

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This Thesis focuses on two main topics. Firstly, we introduce and analyze the novel concept of Systemic Optimal Risk Transfer Equilibrium (SORTE), and we progressively generalize it (i) to a multivariate setup and (ii) to a dynamic (conditional) setting. Additionally we investigate its relation to a recently introduced concept of Systemic Risk Measures (SRM). We present Conditional Systemic Risk Measures and study their properties, dual representation and possible interpretations of the associated allocations as equilibria in the sense of SORTE. On a parallel line of work, we develop a duality
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Hillion, Erwan. "Analyse et géométrie dans les espaces métriques mesurés : inégalités de Borell-Brascamp-Lieb et conjecture de Olkin-Shepp." Toulouse 3, 2010. http://thesesups.ups-tlse.fr/1592/.

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Les travaux menés durant cette thèse sont basés sur la théorie des espaces de longueurs mesurés à courbure de Ricci uniformément minorée initiée par Sturm, Lott et Villani, utilisant de profonds résultats venant de la théorie du transport optimal. Dans une première partie, nous étudions deux familles d'inégalités fonctionnelles, dites de Prékopa-Leindler et de Borell-Brascamp-Lieb, et montrons qu'elles permettent de donner une définition alternative aux bornes sur la courbure de Ricci, satisfaisant un cahier des charges similaire à celui rempli par la condition CD(K,N) de Sturm, Lott et Villan
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Book chapters on the topic "Entropic optimal transport"

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Chewi, Sinho, Jonathan Niles-Weed, and Philippe Rigollet. "Entropic Optimal Transport." In Lecture Notes in Mathematics. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-85160-5_4.

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Stromme, Austin J. "Minimum Intrinsic Dimension Scaling for Entropic Optimal Transport." In Advances in Intelligent Systems and Computing. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65993-5_60.

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Portinale, Lorenzo. "Entropic Regularised Optimal Transport in a Noncommutative Setting." In Bolyai Society Mathematical Studies. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-50466-2_5.

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Pai, Gautam, Gijs Bellaard, Rick Sengers, Luc Florack, and Remco Duits. "Entropic Optimal Transport with Data-Driven Metrics on the Roto-Translation Group." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-92369-2_27.

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Rioul, Olivier. "Optimal Transport to Rényi Entropies." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68445-1_17.

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Yamaka, Woraphon. "Maximum Entropy Learning with Neural Networks." In Optimal Transport Statistics for Economics and Related Topics. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35763-3_8.

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Gu, Wen, Teng Zhang, and Hai Jin. "Entropy Weight Allocation: Positive-unlabeled Learning via Optimal Transport." In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). Society for Industrial and Applied Mathematics, 2022. http://dx.doi.org/10.1137/1.9781611977172.5.

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Tarkhamtham, Payap, and Woraphon Yamaka. "A Generalized Maximum Renyi Entropy Approach in Kink Regression Model." In Credible Asset Allocation, Optimal Transport Methods, and Related Topics. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97273-8_28.

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Carlen, Eric. "Dynamics and Quantum Optimal Transport: Three Lectures on Quantum Entropy and Quantum Markov Semigroups." In Bolyai Society Mathematical Studies. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-50466-2_2.

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Anitha, N., Mohit Tiwari, Shaik Moinuddin Imran, Y. Monikarchana, Manish Kumar Thakur, and M. Clement Joe Anand. "Multi-Criteria Approaches in Selecting Optimal Vehicle-to-Vehicle Communication Protocols." In Networking, Transport, and Quality of Service in Vehicular Networks. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-6422-2.ch008.

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Communication protocols are guidelines governing the transmission of data between the entities of the vehicular networks. These protocols play a crucial role in enabling optimal connectivity, ensuring safety and traffic management. The effective functioning of the V2V network depends on the right choice of communication protocols, as the mismatch in selection results in incompatibility, performance degradation, congestion, and other security issues. This chapter focuses primarily on the intervention of a multi-criteria approach in making an optimal selection of the communication protocols used
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Conference papers on the topic "Entropic optimal transport"

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Wang, Tao, and Ziv Goldfeld. "Neural Estimation of Entropic Optimal Transport." In 2024 IEEE International Symposium on Information Theory (ISIT). IEEE, 2024. http://dx.doi.org/10.1109/isit57864.2024.10619399.

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Reshetova, Daria, Wei-Ning Chen, and Ayfer Özgür. "Training Generative Models from Privatized Data via Entropic Optimal Transport." In 2024 IEEE International Symposium on Information Theory (ISIT). IEEE, 2024. http://dx.doi.org/10.1109/isit57864.2024.10619114.

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Lo, Chi-Jen (Roger), Mahesh K. Marina, Nishanth Sastry, Kai Xu, Saeed Fadaei, and Yong Li. "Shrinking VOD Traffic via Rényi-Entropic Optimal Transport." In SIGMETRICS/PERFORMANCE '24: ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems. ACM, 2024. http://dx.doi.org/10.1145/3652963.3655081.

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Hundrieser, Shayan, Marcel Klatt, and Axel Munk. "Entropic Optimal Transport on Countable Spaces: Statistical Theory and Asymptotics." In Entropy 2021: The Scientific Tool of the 21st Century. MDPI, 2021. http://dx.doi.org/10.3390/entropy2021-09837.

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Yan, Yuguang, Wen Li, Hanrui Wu, Huaqing Min, Mingkui Tan, and Qingyao Wu. "Semi-Supervised Optimal Transport for Heterogeneous Domain Adaptation." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/412.

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Heterogeneous domain adaptation (HDA) aims to exploit knowledge from a heterogeneous source domain to improve the learning performance in a target domain. Since the feature spaces of the source and target domains are different, the transferring of knowledge is extremely difficult. In this paper, we propose a novel semi-supervised algorithm for HDA by exploiting the theory of optimal transport (OT), a powerful tool originally designed for aligning two different distributions. To match the samples between heterogeneous domains, we propose to preserve the semantic consistency between heterogeneou
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Liu, Dong, Minh Thanh Vu, Saikat Chatterjee, and Lars K. Rasmussen. "Entropy-regularized Optimal Transport Generative Models." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8682721.

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Justiniano, Jorge, Andreas Kleiner, Benny Moldovanu, Martin Rumpf, and Philipp Strack. "Entropy-Regularized Optimal Transport in Information Design." In EC '25: 26th ACM Conference on Economics and Computation. ACM, 2025. https://doi.org/10.1145/3736252.3742617.

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Coleman, Todd P., Justin Tantiongloc, Alexis Allegra, Diego Mesa, Dae Kang, and Marcela Mendoza. "Diffeomorphism learning via relative entropy constrained optimal transport." In 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2016. http://dx.doi.org/10.1109/globalsip.2016.7906057.

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Elvander, Filip, Isabel Haasler, Andreas Jakobsson, and Johan Karlsson. "Non-coherent Sensor Fusion via Entropy Regularized Optimal Mass Transport." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8682186.

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Adeyinka, O. B., and G. F. Naterer. "Towards Optical Measurement of Entropy Transport in Turbulent Flows." In 39th AIAA Thermophysics Conference. American Institute of Aeronautics and Astronautics, 2007. http://dx.doi.org/10.2514/6.2007-4052.

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