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Found 6 papers, 3 papers with code
Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning
In this paper, we present a Bayesian active learning method to directly approximate the posterior pdf function of cardiac model parameters, in which we intelligently select training points to query the simulation model in order to learn the posterior pdf using a small number of samples.
Improving Disentangled Representation Learning with the Beta Bernoulli Process
We note that the independence within and the complexity of the latent density are two different properties we constrain when regularizing the posterior density: while the former promotes the disentangling ability of VAE, the latter -- if overly limited -- creates an unnecessary competition with the data reconstruction objective in VAE.
Bayesian Optimization on Large Graphs via a Graph Convolutional Generative Model: Application in Cardiac Model Personalization
In this paper, we present a novel graph convolutional VAE to allow generative modeling of non-Euclidean data, and utilize it to embed Bayesian optimization of large graphs into a small latent space.
Generative Modeling and Inverse Imaging of Cardiac Transmembrane Potential
We introduce a novel model-constrained inference framework that replaces conventional physiological models with a deep generative model trained to generate TMP sequences from low-dimensional generative factors.
Deep Generative Model with Beta Bernoulli Process for Modeling and Learning Confounding Factors
While deep representation learning has become increasingly capable of separating task-relevant representations from other confounding factors in the data, two significant challenges remain.
Learning disentangled representation from 12-lead electrograms: application in localizing the origin of Ventricular Tachycardia
In this work, we present a conditional variational autoencoder (VAE) to extract the subject-specific adjustment to the ECG data, conditioned on task-specific representations learned from a deterministic encoder.
