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