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Found 5 papers, 3 papers with code
HyPer-EP: Meta-Learning Hybrid Personalized Models for Cardiac Electrophysiology
Personalized virtual heart models have demonstrated increasing potential for clinical use, although the estimation of their parameters given patient-specific data remain a challenge.
Unsupervised Learning of Hybrid Latent Dynamics: A Learn-to-Identify Framework
Modern applications increasingly require unsupervised learning of latent dynamics from high-dimensional time-series.
Sequential Latent Variable Models for Few-Shot High-Dimensional Time-Series Forecasting
We compared the presented framework with a comprehensive set of baseline models trained 1) globally on the large meta-training set with diverse dynamics, and 2) individually on single dynamics, both with and without fine-tuning to k-shot support series used by the meta-models.
Few-shot Generation of Personalized Neural Surrogates for Cardiac Simulation via Bayesian Meta-Learning
As test time, metaPNS delivers a personalized neural surrogate by fast feed-forward embedding of a small and flexible number of data available from an individual, achieving -- for the first time -- personalization and surrogate construction for expensive simulations in one end-to-end learning framework.
Neural State-Space Modeling with Latent Causal-Effect Disentanglement
In a novel neural formulation of state-space models (SSMs), we first introduce causal-effect modeling of the latent dynamics via a system of interacting neural ODEs that separately describes 1) the continuous-time dynamics of the internal intervention, and 2) its effect on the trajectory of the system's native state.
