1 citations · 4 across the 10 of their papers we have counts for
10 papers
Overcoming Spurious Solutions in Semi-Dual Neural Optimal Transport: A Smoothing Approach for Learning the Optimal Transport Plan
Jaemoo Choi, Jaewoong Choi, Dohyun Kwon
We address the convergence problem in learning the Optimal Transport (OT) map, where the OT Map refers to a map from one distribution to another while minimizing the transport cost…
Scalable Simulation-free Entropic Unbalanced Optimal Transport
Jaemoo Choi, Jaewoong Choi
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 app…
Robust Barycenter Estimation using Semi-Unbalanced Neural Optimal Transport
Milena Gazdieva, Jaemoo Choi, Alexander Kolesov +3
Aggregating data from multiple sources can be formalized as an Optimal Transport (OT) barycenter problem, which seeks to compute the average of probability distributions with respe…
Unsupervised Point Cloud Completion through Unbalanced Optimal Transport
Taekyung Lee, Jaemoo Choi, Jaewoong Choi +1
Unpaired point cloud completion is crucial for real-world applications, where ground-truth data for complete point clouds are often unavailable. By learning a completion map from u…
Scalable Wasserstein Gradient Flow for Generative Modeling through Unbalanced Optimal Transport
Jaemoo Choi, Jaewoong Choi, Myungjoo Kang
Wasserstein Gradient Flow (WGF) describes the gradient dynamics of probability density within the Wasserstein space. WGF provides a promising approach for conducting optimization o…
Analyzing and Improving Optimal-Transport-based Adversarial Networks
Jaemoo Choi, Jaewoong Choi, Myungjoo Kang
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…
Generative Modeling through the Semi-dual Formulation of Unbalanced Optimal Transport
Jaemoo Choi, Jaewoong Choi, Myungjoo Kang
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 distri…
Restoration based Generative Models
Jaemoo Choi, Yesom Park, Myungjoo Kang
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 th…
Learning PDE Solution Operator for Continuous Modeling of Time-Series
Yesom Park, Jaemoo Choi, Changyeon Yoon +2
Learning underlying dynamics from data is important and challenging in many real-world scenarios. Incorporating differential equations (DEs) to design continuous networks has drawn…
Robust Out-of-Distribution Detection on Deep Probabilistic Generative Models
Jaemoo Choi, Changyeon Yoon, Jeongwoo Bae +1
Out-of-distribution (OOD) detection is an important task in machine learning systems for ensuring their reliability and safety. Deep probabilistic generative models facilitate OOD…