2 citations · 2 across the 8 of their papers we have counts for
10 papers
Predicting Unseen Scientific Interactions: Conditional Flow Models Hallucinate Non-Additive Effects
Sam Gijsen, Moritz Seiler, Kerstin Ritter
Conditional generative models are increasingly used for scientific prediction beyond the direct training support. Yet scientific questions often concern whether one intervention ch…
Beyond Decodability: Do Acoustic Factors Drive Predictions in Speech-Based Alzheimer's Assessment?
Serli Kopar, Alkis Koudounas, Roshan P. Rane +3
Speech-based Alzheimer's disease (AD) assessments increasingly rely on pretrained self-supervised learning (SSL) models that learn acoustic representations directly from raw audio,…
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…
Beyond Binary: Speech Representations Across the Cognitive Score Hierarchy
Serli Kopar, Roshan Prakash Rane, Christian Mychajliw +6
This study examines the relationship between speech representations and the hierarchical structure of cognitive assessment in mild cognitive impairment. Utilizing 5,754 German neur…
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…
EEG-Language Pretraining for Highly Label-Efficient Clinical Phenotyping
Sam Gijsen, Kerstin Ritter
Multimodal language modeling has enabled breakthroughs for representation learning, yet remains unexplored in the realm of functional brain data for clinical phenotyping. This pape…