29 citations · 103 across the 24 of their papers we have counts for
7 papers · 1 filter
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…
Learning Geometry-Dependent and Physics-Based Inverse Image Reconstruction
Xiajun Jiang, Sandesh Ghimire, Jwala Dhamala +3
Deep neural networks have shown great potential in image reconstruction problems in Euclidean space. However, many reconstruction problems involve imaging physics that are dependen…
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…
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…
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…
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…