no code implementations • 13 Oct 2021 • Md Shakil Zaman, Jwala Dhamala, Pradeep Bajracharya, John L. Sapp, B. Milan Horacek, Katherine C. Wu, Natalia A. Trayanova, Linwei Wang
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.
1 code implementation • 3 Sep 2019 • Prashnna Kumar Gyawali, Zhiyuan Li, Cameron Knight, Sandesh Ghimire, B. Milan Horacek, John Sapp, Linwei Wang
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.
1 code implementation • 1 Jul 2019 • Jwala Dhamala, Sandesh Ghimire, John L. Sapp, B. Milan Horacek, Linwei Wang
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.
no code implementations • 12 May 2019 • Sandesh Ghimire, Jwala Dhamala, Prashnna Kumar Gyawali, John L. Sapp, B. Milan Horacek, Linwei Wang
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.
no code implementations • 31 Oct 2018 • Prashnna K Gyawali, Cameron Knight, Sandesh Ghimire, B. Milan Horacek, John L. Sapp, Linwei Wang
While deep representation learning has become increasingly capable of separating task-relevant representations from other confounding factors in the data, two significant challenges remain.
1 code implementation • 4 Aug 2018 • Prashnna K Gyawali, B. Milan Horacek, John L. Sapp, Linwei Wang
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.