7 papers
Controlling for Omitted Variable Bias in Deep Neural Networks
Manuel Pfeuffer, Roshan Prakash Rane, Kerstin Ritter +1
Control variables are widely used in statistical modelling to account for omitted variable bias of known confounders. However, they have largely been underexplored in deep learning…
Motor, Cognitive, or Corpus? What Survives Cross-Lingual Transfer in Speech-Based Parkinsons Disease Detection
Serli Kopar, Sam Gijsen, Abner Hernandez +2
Self-supervised learning (SSL) speech representations achieve strong performance for Parkinson's disease (PD) detection within individual corpora. However, it remains unclear wheth…
Deep Shape Regression for Planar Curves with Multimodal Covariates
Manuel Pfeuffer, Roshan Prakash Rane, Hadya Yassin +2
The shape of a planar curve is the geometric information that remains once translation, rotation, scale and reparametrisation are removed and is of interest in many health applicat…
Measurement noise limits the advantage of nonlinear models over linear models in biomedical prediction
Marc-Andre Schulz, Kerstin Ritter
On biomedical tabular data, flexible models such as deep networks, gradient-boosted trees, and kernel methods are repeatedly matched or beaten by linear and logistic regression giv…
Flow Matching with In-Context Priors for Out-of-Distribution Brain Dynamics
Sam Gijsen, Michał Łukomski, Marc-André Schulz +1
Flow matching and diffusion models enable conditional generation across domains ranging from images to proteins, with recent extensions to out-of-distribution contexts. Yet generat…
Brain-Semantoks: Learning Semantic Tokens of Brain Dynamics with a Self-Distilled Foundation Model
Sam Gijsen, Marc-Andre Schulz, Kerstin Ritter
The development of foundation models for functional magnetic resonance imaging (fMRI) time series holds significant promise for predicting phenotypes related to disease and cogniti…