29 citations · 66 across the 12 of their papers we have counts for
12 papers
Few-shot Generation of Personalized Neural Surrogates for Cardiac Simulation via Bayesian Meta-Learning
Xiajun Jiang, Zhiyuan Li, Ryan Missel +6
Clinical adoption of personalized virtual heart simulations faces challenges in model personalization and expensive computation. While an ideal solution is an efficient neural surr…
Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning
Md Shakil Zaman, Jwala Dhamala, Pradeep Bajracharya +5
Probabilistic estimation of cardiac electrophysiological model parameters serves an important step towards model personalization and uncertain quantification. The expensive computa…
Quantifying the Uncertainty in Model Parameters Using Gaussian Process-Based Markov Chain Monte Carlo: An Application to Cardiac Electrophysiological Models
Jwala Dhamala, John L. Sapp, B. Milan Horácek +1
Estimation of patient-specific model parameters is important for personalized modeling, although sparse and noisy clinical data can introduce significant uncertainty in the estimat…
High-dimensional Bayesian Optimization of Personalized Cardiac Model Parameters via an Embedded Generative Model
Jwala Dhamala, Sandesh Ghimire, John L. Sapp +2
The estimation of patient-specific tissue properties in the form of model parameters is important for personalized physiological models. However, these tissue properties are spatia…
Improving Disentangled Representation Learning with the Beta Bernoulli Process
Prashnna Kumar Gyawali, Zhiyuan Li, Cameron Knight +4
To improve the ability of VAE to disentangle in the latent space, existing works mostly focus on enforcing independence among the learned latent factors. However, the ability of th…
Bayesian Optimization on Large Graphs via a Graph Convolutional Generative Model: Application in Cardiac Model Personalization
Jwala Dhamala, Sandesh Ghimire, John L. Sapp +2
Personalization of cardiac models involves the optimization of organ tissue properties that vary spatially over the non-Euclidean geometry model of the heart. To represent the high…