5 citations · 5 across the 12 of their papers we have counts for
4 papers · 1 filter
REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability
Kristoffer K. Wickstrøm, Thea Brüsch, Michael C. Kampffmeyer +1
Incorporating uncertainty is crucial to provide trustworthy explanations of deep learning models. Recent works have demonstrated how uncertainty modeling can be particularly import…
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…
Generalized Cauchy-Schwarz Divergence and Its Deep Learning Applications
Mingfei Lu, Chenxu Li, Shujian Yu +2
Divergence measures play a central role and become increasingly essential in deep learning, yet efficient measures for multiple (more than two) distributions are rarely explored. T…
FreqRISE: Explaining time series using frequency masking
Thea Brüsch, Kristoffer Knutsen Wickstrøm, Mikkel N. Schmidt +2
Time-series data are fundamentally important for many critical domains such as healthcare, finance, and climate, where explainable models are necessary for safe automated decision…