2 papers
eess.SP2026
A comprehensive evaluation of pretraining strategies for channel-agnostic contrastive self-supervision of biosignals
Thea Brüsch, Mikkel N. Schmidt, Tommy S. Alstrøm
Contrastive learning yields impressive results for self-supervision in computer vision. The approach relies on the creation of positive pairs, something which is often achieved thr…
cs.LG2025
FLEXtime: Filterbank learning to explain time series
Thea Brüsch, Kristoffer K. Wickstrøm, Mikkel N. Schmidt +2
State-of-the-art methods for explaining predictions from time series involve learning an instance-wise saliency mask for each time step; however, many types of time series are diff…