29 citations · 52 across the 7 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…
Improving Generalization of Sequence Encoder-Decoder Networks for Inverse Imaging of Cardiac Transmembrane Potential
Sandesh Ghimire, Prashnna Kumar Gyawali, John L Sapp +2
Deep learning models have shown state-of-the-art performance in many inverse reconstruction problems. However, it is not well understood what properties of the latent representatio…