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Found 13 papers, 8 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.
Deep learning and differential equations for modeling changes in individual-level latent dynamics between observation periods
These serve as interpretable resilience-related outcomes, to be predicted from characteristics of individuals, measured at baseline and a follow-up time point, and selecting a small set of important predictors.
Adapting deep generative approaches for getting synthetic data with realistic marginal distributions
While there are extensions that assume other distributions for the latent space, this does not generally increase flexibility for data with many different distributions.
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.
Is there a role for statistics in artificial intelligence?
The research on and application of artificial intelligence (AI) has triggered a comprehensive scientific, economic, social and political discussion.
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.
Deep generative models in DataSHIELD
Such data sets of artificial patients, which are not linked to real patients, can then be used for joint analyses.
Distributed Multivariate Regression Modeling For Selecting Biomarkers Under Data Protection Constraints
To minimize the amount of transferred data and the number of calls, we also provide a heuristic variant of the approach.
Modeling Activity Tracker Data Using Deep Boltzmann Machines
To investigate the feasibility of deep learning approaches for unsupervised learning with such data, we examine weekly usage patterns of Fitbit activity trackers with deep Boltzmann machines (DBMs).
The Evolution of Boosting Algorithms - From Machine Learning to Statistical Modelling
This review article attempts to highlight this evolution of boosting algorithms from machine learning to statistical modelling.
Methodology
