29 citations · 80 across the 24 of their papers we have counts for
6 papers · 1 filter
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…
Semi-Supervised Learning by Disentangling and Self-Ensembling Over Stochastic Latent Space
Prashnna Kumar Gyawali, Zhiyuan Li, Sandesh Ghimire +1
The success of deep learning in medical imaging is mostly achieved at the cost of a large labeled data set. Semi-supervised learning (SSL) provides a promising solution by leveragi…
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…
A Variational Approach to Sparse Model Error Estimation in Cardiac Electrophysiological Imaging
Sandesh Ghimire, John L Sapp, Milan Horacek +1
Noninvasive reconstruction of cardiac electrical activity from surface electrocardiograms (ECG) involves solving an ill-posed inverse problem. Cardiac electrophysiological (EP) mod…
Generative Modeling and Inverse Imaging of Cardiac Transmembrane Potential
Sandesh Ghimire, Jwala Dhamala, Prashnna Kumar Gyawali +3
Noninvasive reconstruction of cardiac transmembrane potential (TMP) from surface electrocardiograms (ECG) involves an ill-posed inverse problem. Model-constrained regularization is…
Improving Generalization of Deep Networks for Inverse Reconstruction of Image Sequences
Sandesh Ghimire, Prashnna Kumar Gyawali, Jwala Dhamala +3
Deep learning networks have shown state-of-the-art performance in many image reconstruction problems. However, it is not well understood what properties of representation and learn…