3 citations · 3 across the 5 of their papers we have counts for
6 papers
Meta-iLaD: Identifiable Latent Dynamics via Meta-Learning of Dynamics Environments
Yubo Ye, Sweekar Piya, Xiajun Jiang +1
Learning latent dynamics is central to assessing the current states and forecasting the future trajectories of high-dimensional time series. For locally-stationary latent dynamics…
Learning Cardiac Electrophysiology Digital Twins Through Agentic Discovery of Hybrid Structure
Ziqi Zhou, Yubo Ye, Sumeet Atul Vadhavka +2
Building personalized cardiac electrophysiology (EP) digital twins requires identifying the appropriate model structure for each patient, not merely fitting parameters. Traditional…
MeiBRD: Meta-Learning Intraoperative Biomechanical Residual Deformation
Casey Meisenzahl, Jon Heiselman, Michael Holtz +3
Accurate intraoperative liver registration is challenging due to substantial soft-tissue deformation yet sparse intraoperative measurements. Biomechanical models regularize this il…
HyPer-EP: Meta-Learning Hybrid Personalized Models for Cardiac Electrophysiology
Xiajun Jiang, Sumeet Vadhavkar, Yubo Ye +3
Personalized virtual heart models have demonstrated increasing potential for clinical use, although the estimation of their parameters given patient-specific data remain a challeng…
Unsupervised Learning of Hybrid Latent Dynamics: A Learn-to-Identify Framework
Yubo Ye, Sumeet Vadhavkar, Xiajun Jiang +3
Modern applications increasingly require unsupervised learning of latent dynamics from high-dimensional time-series. This presents a significant challenge of identifiability: many…
Hybrid Kinetics Embedding Framework for Dynamic PET Reconstruction
Yubo Ye, Huafeng Liu, Linwei Wang
In dynamic positron emission tomography (PET) reconstruction, the importance of leveraging the temporal dependence of the data has been well appreciated. Current deep-learning solu…