29 citations · 75 across the 14 of their papers we have counts for
12 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…
Neural State-Space Modeling with Latent Causal-Effect Disentanglement
Maryam Toloubidokhti, Ryan Missel, Xiajun Jiang +2
Despite substantial progress in deep learning approaches to time-series reconstruction, no existing methods are designed to uncover local activities with minute signal strength due…
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…
Enhancing Mixup-based Semi-Supervised Learning with Explicit Lipschitz Regularization
Prashnna Kumar Gyawali, Sandesh Ghimire, Linwei Wang
The success of deep learning relies on the availability of large-scale annotated data sets, the acquisition of which can be costly, requiring expert domain knowledge. Semi-supervis…
Semi-supervised Medical Image Classification with Global Latent Mixing
Prashnna Kumar Gyawali, Sandesh Ghimire, Pradeep Bajracharya +2
Computer-aided diagnosis via deep learning relies on large-scale annotated data sets, which can be costly when involving expert knowledge. Semi-supervised learning (SSL) mitigates…
Progressive Learning and Disentanglement of Hierarchical Representations
Zhiyuan Li, Jaideep Vitthal Murkute, Prashnna Kumar Gyawali +1
Learning rich representation from data is an important task for deep generative models such as variational auto-encoder (VAE). However, by extracting high-level abstractions in the…