4 citations · 4 across the 3 of their papers we have counts for
4 papers · 1 filter
What masking geometry works best for EEG foundation models?
Pierre Guetschel, Bruno Aristimunha, Yassine El Ouahidi +3
EEG foundation models hold promise for scalable brain-signal decoding across clinical and cognitive neuroscience applications, yet their pre-training pipelines remain poorly unders…
Crossing the Validation Crisis: Cross-Validation Reduces Benchmarking Variance Surprisingly Well
Célestin Eve, Gaël Varoquaux, Thomas Moreau
Modern machine learning progresses through empirical work, benchmarking new methods to evaluate relative performance. However, the statistical variability inherent to evaluation -…
SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities
Yanis Lalou, Théo Gnassounou, Antoine Collas +6
Unsupervised Domain Adaptation (DA) consists of adapting a model trained on a labeled source domain to perform well on an unlabeled target domain with some data distribution shift.…
S-JEPA: towards seamless cross-dataset transfer through dynamic spatial attention
Pierre Guetschel, Thomas Moreau, Michael Tangermann
Motivated by the challenge of seamless cross-dataset transfer in EEG signal processing, this article presents an exploratory study on the use of Joint Embedding Predictive Architec…