2 citations · 2 across the 8 of their papers we have counts for
10 papers
Embedding interpretable -regression into neural networks for uncovering temporal structure in cell imaging
Fabian Kabus, Maren Hackenberg, Julia Hindel +6
While artificial neural networks excel in unsupervised learning of non-sparse structure, classical statistical regression techniques offer better interpretability, in particular wh…
A statistical perspective on transformers for small longitudinal cohort data
Kiana Farhadyar, Maren Hackenberg, Kira Ahrens +10
Modeling of longitudinal cohort data typically involves complex temporal dependencies between multiple variables. There, the transformer architecture, which has been highly success…
Using latent representations to link disjoint longitudinal data for mixed-effects regression
Clemens Schächter, Maren Hackenberg, Michelle Pfaffenlehner +6
Many rare diseases offer limited established treatment options, leading patients to switch therapies when new medications emerge. To analyze the impact of such treatment switches w…
Small Data Explainer -- The impact of small data methods in everyday life
Maren Hackenberg, Sophia G. Connor, Fabian Kabus +10
The emergence of breakthrough artificial intelligence (AI) techniques has led to a renewed focus on how small data settings, i.e., settings with limited information, can benefit fr…
Combining propensity score methods with variational autoencoders for generating synthetic data in presence of latent sub-groups
Kiana Farhadyar, Federico Bonofiglio, Maren Hackenberg +2
In settings requiring synthetic data generation based on a clinical cohort, e.g., due to data protection regulations, heterogeneity across individuals might be a nuisance that we n…
Investigating a domain adaptation approach for integrating different measurement instruments in a longitudinal clinical registry
Maren Hackenberg, Michelle Pfaffenlehner, Max Behrens +3
In a longitudinal clinical registry, different measurement instruments might have been used for assessing individuals at different time points. To combine them, we investigate deep…
A statistical approach to latent dynamic modeling with differential equations
Maren Hackenberg, Astrid Pechmann, Clemens Kreutz +2
Ordinary differential equations (ODEs) can provide mechanistic models of temporally local changes of processes, where parameters are often informed by external knowledge. While ODE…
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…
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…