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cs.LG2026
Mechanistic Interpretability of EEG Foundation Models via Sparse Autoencoders
William Lehn-Schiøler, Magnus Ruud Kjær, Rahul Thapa +10
EEG foundation models achieve state-of-the-art clinical performance, yet the internal computations driving their predictions remain opaque: a barrier to clinical trust. We apply To…
cs.LG2026
Pretraining on Sleep Data Improves non-Sleep Biosignal Tasks
William Lehn-Schiøler, Magnus Ruud Kjær, Phillip Hempel +7
Sleep foundation models have recently demonstrated strong performance on in-domain polysomnography tasks, including sleep staging, apnea detection, and disease risk prediction. In…
cs.LG2024
Connecting Concept Convexity and Human-Machine Alignment in Deep Neural Networks
Teresa Dorszewski, Lenka TÄtková, Lorenz Linhardt +1
Understanding how neural networks align with human cognitive processes is a crucial step toward developing more interpretable and reliable AI systems. Motivated by theories of huma…