28 citations · 121 across the 16 of their papers we have counts for
9 papers · 1 filter
Learnable Time-Frequency Masks for Explaining Time-Series Classifiers
Theresa Dahl Frehr, Francisco Pelayo, Lukas Raad +3
Time-series explainability remains challenging because discriminative information is often encoded in latent frequency or time-frequency features rather than in the raw signal itse…
On What We Can Learn from Low-Resolution Data
Theresa Dahl Frehr, Niels Henrik Pontoppidan, Hiba Nassar +1
Artificial intelligence systems typically rely on large, centrally collected datasets, a premise that does not hold in many real-world domains such as healthcare and public institu…
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…
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…
Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity
Bo Li, Yasin Esfandiari, Mikkel N. Schmidt +2
In federated learning, data heterogeneity is a critical challenge. A straightforward solution is to shuffle the clients' data to homogenize the distribution. However, this may viol…
On convex decision regions in deep network representations
Lenka Tětková, Thea Brüsch, Teresa Karen Scheidt +5
Current work on human-machine alignment aims at understanding machine-learned latent spaces and their correspondence to human representations. G{ä}rdenfors' conceptual spaces is a…
On the effectiveness of partial variance reduction in federated learning with heterogeneous data
Bo Li, Mikkel N. Schmidt, Tommy S. Alstrøm +1
Data heterogeneity across clients is a key challenge in federated learning. Prior works address this by either aligning client and server models or using control variates to correc…
Raman Spectrum Matching with Contrastive Representation Learning
Bo Li, Mikkel N. Schmidt, Tommy S. Alstrøm
Raman spectroscopy is an effective, low-cost, non-intrusive technique often used for chemical identification. Typical approaches are based on matching observations to a reference d…
Road Roughness Estimation Using Machine Learning
Milena Bajic, Shahrzad M. Pour, Asmus Skar +3
Road roughness is a very important road condition for the infrastructure, as the roughness affects both the safety and ride comfort of passengers. The roads deteriorate over time w…