Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Choi, Jaemoo"'
Autor:
Gazdieva, Milena, Choi, Jaemoo, Kolesov, Alexander, Choi, Jaewoong, Mokrov, Petr, Korotin, Alexander
A common challenge in aggregating data from multiple sources can be formalized as an \textit{Optimal Transport} (OT) barycenter problem, which seeks to compute the average of probability distributions with respect to OT discrepancies. However, the pr
Externí odkaz:
http://arxiv.org/abs/2410.03974
Optimal Transport (OT) theory investigates the cost-minimizing transport map that moves a source distribution to a target distribution. Recently, several approaches have emerged for learning the optimal transport map for a given cost function using n
Externí odkaz:
http://arxiv.org/abs/2410.03783
Unpaired point cloud completion explores methods for learning a completion map from unpaired incomplete and complete point cloud data. In this paper, we propose a novel approach for unpaired point cloud completion using the unbalanced optimal transpo
Externí odkaz:
http://arxiv.org/abs/2410.02671
Autor:
Choi, Jaemoo, Choi, Jaewoong
The Optimal Transport (OT) problem investigates a transport map that connects two distributions while minimizing a given cost function. Finding such a transport map has diverse applications in machine learning, such as generative modeling and image-t
Externí odkaz:
http://arxiv.org/abs/2410.02656
Wasserstein Gradient Flow (WGF) describes the gradient dynamics of probability density within the Wasserstein space. WGF provides a promising approach for conducting optimization over the probability distributions. Numerically approximating the conti
Externí odkaz:
http://arxiv.org/abs/2402.05443
Optimal Transport (OT) problem aims to find a transport plan that bridges two distributions while minimizing a given cost function. OT theory has been widely utilized in generative modeling. In the beginning, OT distance has been used as a measure fo
Externí odkaz:
http://arxiv.org/abs/2310.02611
Optimal Transport (OT) problem investigates a transport map that bridges two distributions while minimizing a given cost function. In this regard, OT between tractable prior distribution and data has been utilized for generative modeling tasks. Howev
Externí odkaz:
http://arxiv.org/abs/2305.14777
Denoising diffusion models (DDMs) have recently attracted increasing attention by showing impressive synthesis quality. DDMs are built on a diffusion process that pushes data to the noise distribution and the models learn to denoise. In this paper, w
Externí odkaz:
http://arxiv.org/abs/2303.05456
Learning underlying dynamics from data is important and challenging in many real-world scenarios. Incorporating differential equations (DEs) to design continuous networks has drawn much attention recently, however, most prior works make specific assu
Externí odkaz:
http://arxiv.org/abs/2302.00854
Publikováno v:
In Journal of Theoretical Biology 7 November 2024 594
