2 citations · 2 across the 5 of their papers we have counts for
7 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,…
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…
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…