376 citations · 472 across the 11 of their papers we have counts for
14 papers
Deep learning and differential equations for modeling changes in individual-level latent dynamics between observation periods
Göran Köber, Raffael Kalisch, Lara Puhlmann +3
When modeling longitudinal biomedical data, often dimensionality reduction as well as dynamic modeling in the resulting latent representation is needed. This can be achieved by art…
Adapting deep generative approaches for getting synthetic data with realistic marginal distributions
Kiana Farhadyar, Federico Bonofiglio, Daniela Zoeller +1
Synthetic data generation is of great interest in diverse applications, such as for privacy protection. Deep generative models, such as variational autoencoders (VAEs), are a popul…
Using Differentiable Programming for Flexible Statistical Modeling
Maren Hackenberg, Marlon Grodd, Clemens Kreutz +5
Differentiable programming has recently received much interest as a paradigm that facilitates taking gradients of computer programs. While the corresponding flexible gradient-based…
Deep dynamic modeling with just two time points: Can we still allow for individual trajectories?
Maren Hackenberg, Philipp Harms, Michelle Pfaffenlehner +4
Longitudinal biomedical data are often characterized by a sparse time grid and individual-specific development patterns. Specifically, in epidemiological cohort studies and clinica…
Is there a role for statistics in artificial intelligence?
Sarah Friedrich, Gerd Antes, Sigrid Behr +11
The research on and application of artificial intelligence (AI) has triggered a comprehensive scientific, economic, social and political discussion. Here we argue that statistics,…
Transfer learning of regression models from a sequence of datasets by penalized estimation
Wessel N. van Wieringen, Harald Binder
Transfer learning refers to the promising idea of initializing model fits based on pre-training on other data. We particularly consider regression modeling settings where parameter…