17 citations · 18 across the 8 of their papers we have counts for
8 papers
Model synthesis and identifiability analysis of stiff chemical reaction systems with inVAErt networks
Sreejata Dey, Guoxiang Grayson Tong, Jonathan F. MacArt +1
We consider the problem of learning data-driven replicas for stiff systems of ordinary differential equations arising in chemical kinetics that can be evaluated with high computati…
FalconBC: Flow matching for Amortized inference of Latent-CONditioned physiologic Boundary Conditions
Chloe H. Choi, Alison L. Marsden, Daniele E. Schiavazzi
Boundary condition tuning is a fundamental step in patient-specific cardiovascular modeling. Despite an increase in offline training cost, recent methods in data-driven variational…
Conditional Normalizing Flows for Forward and Backward Joint State and Parameter Estimation
Luke S. Lagunowich, Guoxiang Grayson Tong, Daniele E. Schiavazzi
Traditional filtering algorithms for state estimation -- such as classical Kalman filtering, unscented Kalman filtering, and particle filters -- show performance degradation when a…
On the accuracy of implicit neural representations for cardiovascular anatomies and hemodynamic fields
Jubilee Lee, Daniele E. Schiavazzi
Implicit neural representations (INRs, also known as neural fields) have recently emerged as a powerful framework for knowledge representation, synthesis, and compression. By encod…
LINFA: a Python library for variational inference with normalizing flow and annealing
Yu Wang, Emma R. Cobian, Jubilee Lee +3
Variational inference is an increasingly popular method in statistics and machine learning for approximating probability distributions. We developed LINFA (Library for Inference wi…
Differentially Private Normalizing Flows for Density Estimation, Data Synthesis, and Variational Inference with Application to Electronic Health Records
Bingyue Su, Yu Wang, Daniele E. Schiavazzi +1
Electronic health records (EHR) often contain sensitive medical information about individual patients, posing significant limitations to sharing or releasing EHR data for downstrea…
AdaAnn: Adaptive Annealing Scheduler for Probability Density Approximation
Emma R. Cobian, Jonathan D. Hauenstein, Fang Liu +1
Approximating probability distributions can be a challenging task, particularly when they are supported over regions of high geometrical complexity or exhibit multiple modes. Annea…
Variational Inference with NoFAS: Normalizing Flow with Adaptive Surrogate for Computationally Expensive Models
Yu Wang, Fang Liu, Daniele E. Schiavazzi
Fast inference of numerical model parameters from data is an important prerequisite to generate predictive models for a wide range of applications. Use of sampling-based approaches…