8 citations · 19 across the 5 of their papers we have counts for
5 papers
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…
Deep Generative Model with Beta Bernoulli Process for Modeling and Learning Confounding Factors
Prashnna K Gyawali, Cameron Knight, Sandesh Ghimire +3
While deep representation learning has become increasingly capable of separating task-relevant representations from other confounding factors in the data, two significant challenge…
Learning disentangled representation from 12-lead electrograms: application in localizing the origin of Ventricular Tachycardia
Prashnna K Gyawali, B. Milan Horacek, John L. Sapp +1
The increasing availability of electrocardiogram (ECG) data has motivated the use of data-driven models for automating various clinical tasks based on ECG data. The development of…