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This work studies an explicit embedding of the set of probability measures into a Hilbert space, defined using optimal transport maps from a reference probability density. This embedding linearizes to some extent the 2-Wasserstein space,…

Machine Learning · Statistics 2022-05-05 Quentin Mérigot , Alex Delalande , Frédéric Chazal

Recent advances in time series classification have largely focused on methods that either employ deep learning or utilize other machine learning models for feature extraction. Though successful, their power often comes at the requirement of…

Machine Learning · Computer Science 2021-01-13 Robert J. Ravier , Mohammadreza Soltani , Miguel Simões , Denis Garagic , Vahid Tarokh

Generating samples given a specific label requires estimating conditional distributions. We derive a tractable upper bound of the Wasserstein distance between conditional distributions to lay the theoretical groundwork to learn conditional…

Machine Learning · Statistics 2023-08-29 Young-geun Kim , Kyungbok Lee , Youngwon Choi , Joong-Ho Won , Myunghee Cho Paik

In this paper, we introduce a new generalization of geometric distribution which can also viewed as discrete analogue of weighted exponential distribution introduced by Gupta and Kundu(2009). We study some basic distributional properties…

Methodology · Statistics 2016-02-08 Deepesh Bhati , Savitri Joshi

Gromov--Wasserstein (GW) distances compare graphs, shapes, and point clouds through internal distances, without requiring a common coordinate system. This invariance is powerful, but discrete GW is a nonconvex quadratic optimal transport…

Machine Learning · Computer Science 2026-05-15 Ao Xu , Tieru Wu

Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for structured/relational reasoning, typically in low dimensions.…

Artificial Intelligence · Computer Science 2023-04-25 Bo Xiong , Mojtaba Nayyeri , Ming Jin , Yunjie He , Michael Cochez , Shirui Pan , Steffen Staab

Mixed effect modeling for longitudinal data is challenging when the observed data are random objects, which are complex data taking values in a general metric space without linear structure. In such settings the classical additive error…

Methodology · Statistics 2023-07-13 Satarupa Bhattacharjee , Hans-Georg Müller

Given two points of a Generalized Robertson-Walker spacetime, the existence, multiplicity and causal character of geodesic connecting them is characterized. Conjugate points of such geodesics are related to conjugate points of geodesics on…

Differential Geometry · Mathematics 2009-10-31 J. L. Flores , M. Sanchez

The derivation of the phase transition in the model of Ga\'zdzicki and Gorenstein is generalized and simplified by using a geometrical construction.

Nuclear Theory · Physics 2017-03-08 Kacper Zalewski

We propose a novel approach for comparing distributions whose supports do not necessarily lie on the same metric space. Unlike Gromov-Wasserstein (GW) distance which compares pairwise distances of elements from each distribution, we…

Machine Learning · Statistics 2021-04-23 Mokhtar Z. Alaya , Maxime Bérar , Gilles Gasso , Alain Rakotomamonjy

In the Ashtekar and geometrodynamic formulations of vacuum general relativity, the Euclidean and Lorentzian sectors can be related by means of the generalized Wick transform discovered by Thiemann. For some vacuum gravitational systems in…

General Relativity and Quantum Cosmology · Physics 2008-02-03 Guillermo A. Mena Marugan

The movement changes the underlying spatial representation of the participated mobile objects or nodes. In real world scenario, such mobile nodes can be part of any biological network, transportation network, social network, human…

Social and Information Networks · Computer Science 2024-07-29 Md. Arquam , Utkarsh Tiwari , Suchi Kumari

To explore the properties of space and initial singularities in the context of general relativity, where spacetime becomes poorly defined and no longer belongs to a regular manifold, we examine the evolution of the expansion of timelike…

General Relativity and Quantum Cosmology · Physics 2025-04-04 Abdel Nasser Tawfik , Azzah A. Alshehri , Antonio Pasqua

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. We use this generalized…

Analysis of PDEs · Mathematics 2015-06-05 Benedetto Piccoli , Francesco Rossi

Relational representation learning transforms relational data into continuous and low-dimensional vector representations. However, vector-based representations fall short in capturing crucial properties of relational data that are complex…

Machine Learning · Computer Science 2024-09-25 Bo Xiong

Transformer models have consistently achieved remarkable results in various domains such as natural language processing and computer vision. However, despite ongoing research efforts to better understand these models, the field still lacks…

Machine Learning · Computer Science 2024-10-18 Ilya Kaufman , Omri Azencot

It has long been thought that high-dimensional data encountered in many practical machine learning tasks have low-dimensional structure, i.e., the manifold hypothesis holds. A natural question, thus, is to estimate the intrinsic dimension…

Machine Learning · Statistics 2022-06-01 Adam Block , Zeyu Jia , Yury Polyanskiy , Alexander Rakhlin

There exists a correlation between geospatial activity temporal patterns and type of land use. A novel self-supervised approach is proposed to stratify landscape based on mobility activity time series. First, the time series signal is…

Computer Vision and Pattern Recognition · Computer Science 2024-01-18 Yi Cao , Swetava Ganguli , Vipul Pandey

There exists a correlation between geospatial activity temporal patterns and type of land use. A novel self-supervised approach is proposed to stratify landscape based on mobility activity time series. First, the time series signal is…

Artificial Intelligence · Computer Science 2023-04-27 Yi Cao , Swetava Ganguli , Vipul Pandey

This article provides an overview of various notions of shape spaces, including the space of parametrized and unparametrized curves, the space of immersions, the diffeomorphism group and the space of Riemannian metrics. We discuss the…

Differential Geometry · Mathematics 2014-10-07 Martin Bauer , Martins Bruveris , Peter W. Michor
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