89 citations · 388 across the 81 of their papers we have counts for
12 papers · 1 filter
Diffusion-Augmented Coreset Expansion for Scalable Dataset Distillation
Ali Abbasi, Shima Imani, Chenyang An +6
With the rapid scaling of neural networks, data storage and communication demands have intensified. Dataset distillation has emerged as a promising solution, condensing information…
Understanding Learning with Sliced-Wasserstein Requires Rethinking Informative Slices
Huy Tran, Yikun Bai, Ashkan Shahbazi +2
The practical applications of Wasserstein distances (WDs) are constrained by their sample and computational complexities. Sliced-Wasserstein distances (SWDs) provide a workaround b…
Linear Spherical Sliced Optimal Transport: A Fast Metric for Comparing Spherical Data
Xinran Liu, Yikun Bai, Rocío Díaz Martín +5
Efficient comparison of spherical probability distributions becomes important in fields such as computer vision, geosciences, and medicine. Sliced optimal transport distances, such…
Expected Sliced Transport Plans
Xinran Liu, Rocío Díaz Martín, Yikun Bai +4
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 t…
Linear Partial Gromov-Wasserstein Embedding
Yikun Bai, Abihith Kothapalli, Hengrong Du +2
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…
NeuroBOLT: Resting-state EEG-to-fMRI Synthesis with Multi-dimensional Feature Mapping
Yamin Li, Ange Lou, Ziyuan Xu +7
Functional magnetic resonance imaging (fMRI) is an indispensable tool in modern neuroscience, providing a non-invasive window into whole-brain dynamics at millimeter-scale spatial…