376 citations · 472 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
The JuliaConnectoR: a functionally oriented interface for integrating Julia in R
Stefan Lenz, Maren Hackenberg, Harald Binder
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 p…
Deep generative models in DataSHIELD
Stefan Lenz, Harald Binder
The best way to calculate statistics from medical data is to use the data of individual patients. In some settings, this data is difficult to obtain due to privacy restrictions. In…