Pradeep Bajracharya
Pradeep Bajracharya
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Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning
We present a Bayesian active learning method to directly approximate the posterior pdf function of cardiac model parameters, in which we intelligently select training points to query the simulation model in order to learn the posterior pdf using a small number of samples. We integrate a generative model into Bayesian active learning to allow approximating posterior pdf of high-dimensional model parameters at the resolution of the cardiac mesh.
Md Shakil Zaman
,
Jwala Dhamala
,
Pradeep Bajracharya
,
John L. Sapp
,
B Milan Horácek
,
Katherine C Wu
,
Natalia A Trayanova
,
Linwei Wang
Embedding High-dimensional Bayesian Optimization via Generative Modeling - Parameter Personalization of Cardiac Electrophysiological Models
We present a novel concept that uses a generative variational auto-encoder (VAE) to embed HD Bayesian optimization into a low-dimensional (LD) latent space that represents the generative code of HD parameters. We further utilize VAE-encoded knowledge about the generative code to guide the exploration of the search space.
Jwala Dhamala
,
Pradeep Bajracharya
,
Hermenegild J Arevalo
,
B Milan Horácek
,
Katherine C Wu
,
Natalia A Trayanova
,
Linwei Wang
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