Search Results for author:
Found 6 papers, 5 papers with code
Combining propensity score methods with variational autoencoders for generating synthetic data in presence of latent sub-groups
The sources of such heterogeneity might be known, e. g., as indicated by sub-groups labels, or might be unknown and thus reflected only in properties of distributions, such as bimodality or skewness.
Investigating a domain adaptation approach for integrating different measurement instruments in a longitudinal clinical registry
In a longitudinal clinical registry, different measurement instruments might have been used for assessing individuals at different time points.
A statistical approach to latent dynamic modeling with differential equations
Neural networks are used for obtaining a low-dimensional latent space for dynamic modeling from a potentially large number of variables, and for obtaining patient-specific ODE parameters from baseline variables.
Using Differentiable Programming for Flexible Statistical Modeling
Differentiable programming has recently received much interest as a paradigm that facilitates taking gradients of computer programs.
Deep dynamic modeling with just two time points: Can we still allow for individual trajectories?
Inspired by recent advances that allow to combine deep learning with dynamic modeling, we investigate whether such approaches can be useful for uncovering complex structure, in particular for an extreme small data setting with only two observations time points for each individual.
The JuliaConnectoR: a functionally oriented interface for integrating Julia in R
Like many groups considering the new programming language Julia, we faced the challenge of accessing the algorithms that we develop in Julia from R. Therefore, we developed the R package JuliaConnectoR, available from the CRAN repository and GitHub (https://github. com/stefan-m-lenz/JuliaConnectoR), in particular for making advanced deep learning tools available.
