Zobrazeno 1 - 10
of 353
pro vyhledávání: '"Rocío, Díaz"'
Autor:
Liu, Xinran, Bai, Yikun, Martín, Rocío Díaz, Shi, Kaiwen, Shahbazi, Ashkan, Landman, Bennett A., Chang, Catie, Kolouri, Soheil
Efficient comparison of spherical probability distributions becomes important in fields such as computer vision, geosciences, and medicine. Sliced optimal transport distances, such as spherical and stereographic spherical sliced Wasserstein distances
Externí odkaz:
http://arxiv.org/abs/2411.06055
The Gromov Wasserstein (GW) problem, a variant of the classical optimal transport (OT) problem, has attracted growing interest in the machine learning and data science communities due to its ability to quantify similarity between measures in differen
Externí odkaz:
http://arxiv.org/abs/2410.16669
Autor:
Liu, Xinran, Martín, Rocío Díaz, Bai, Yikun, Shahbazi, Ashkan, Thorpe, Matthew, Aldroubi, Akram, Kolouri, Soheil
The optimal transport (OT) problem has gained significant traction in modern machine learning for its ability to: (1) provide versatile metrics, such as Wasserstein distances and their variants, and (2) determine optimal couplings between probability
Externí odkaz:
http://arxiv.org/abs/2410.12176
Optimal transport has been used to define bijective nonlinear transforms and different transport-related metrics for discriminating data and signals. Here we briefly describe the advances in this topic with the main applications and properties in eac
Externí odkaz:
http://arxiv.org/abs/2406.15503
Autor:
Bai, Yikun, Martin, Rocio Diaz, Kothapalli, Abihith, Du, Hengrong, Liu, Xinran, Kolouri, Soheil
The Gromov-Wasserstein (GW) distance has gained increasing interest in the machine learning community in recent years, as it allows for the comparison of measures in different metric spaces. To overcome the limitations imposed by the equal mass requi
Externí odkaz:
http://arxiv.org/abs/2402.03664
Autor:
Tran, Huy, Bai, Yikun, Kothapalli, Abihith, Shahbazi, Ashkan, Liu, Xinran, Martin, Rocio Diaz, Kolouri, Soheil
Comparing spherical probability distributions is of great interest in various fields, including geology, medical domains, computer vision, and deep representation learning. The utility of optimal transport-based distances, such as the Wasserstein dis
Externí odkaz:
http://arxiv.org/abs/2402.02345
In this paper, we address the problem of recovering constant source terms in a discrete dynamical system represented by $x_{n+1} = Ax_n + w$, where $x_n$ is the $n$-th state in a Hilbert space $\mathcal{H}$, $A$ is a bounded linear operator in $\math
Externí odkaz:
http://arxiv.org/abs/2401.15450
Autor:
Martin, Rocio Diaz, Medri, Ivan, Bai, Yikun, Liu, Xinran, Yan, Kangbai, Rohde, Gustavo K., Kolouri, Soheil
The optimal transport problem for measures supported on non-Euclidean spaces has recently gained ample interest in diverse applications involving representation learning. In this paper, we focus on circular probability measures, i.e., probability mea
Externí odkaz:
http://arxiv.org/abs/2310.06002
In this paper, we investigate the problem of source recovery in a dynamical system utilizing space-time samples. This is a specific issue within the broader field of dynamical sampling, which involves collecting samples from solutions to a differenti
Externí odkaz:
http://arxiv.org/abs/2308.01462
Optimal transport (OT) has gained popularity due to its various applications in fields such as machine learning, statistics, and signal processing. However, the balanced mass requirement limits its performance in practical problems. To address these
Externí odkaz:
http://arxiv.org/abs/2302.03232