3 papers
cs.LG2026
Structure-Preserving Correction Learning for Sparse Bayesian Inference in Brain Source Imaging
Marco Morik, Xiao Ruiting, Shinichi Nakajima +2
Classical sparse Type-II Bayesian methods for M/EEG brain imaging support joint estimation of source and noise hyperparameters, but rely on fixed iterative update rules. Although t…
eess.IV2026
Enhancing Brain Source Reconstruction by Initializing 3D Neural Networks with Physical Inverse Solutions
Marco Morik, Ali Hashemi, Klaus-Robert Müller +2
Reconstructing brain sources is a fundamental challenge in neuroscience, crucial for understanding brain function and dysfunction. Electroencephalography (EEG) signals have a high…
cs.LG2025
Minimizing False-Positive Attributions in Explanations of Non-Linear Models
Anders Gjølbye, Stefan Haufe, Lars Kai Hansen
Suppressor variables can influence model predictions without being dependent on the target outcome, and they pose a significant challenge for Explainable AI (XAI) methods. These va…