84 citations · 325 across the 22 of their papers we have counts for
6 papers · 1 filter
Modeling Human Responses by Ordinal Archetypal Analysis
Anna Emilie J. Wedenborg, Michael Alexander Harborg, Andreas Bigom +6
This paper introduces a novel framework for Archetypal Analysis (AA) tailored to ordinal data, particularly from questionnaires. Unlike existing methods, the proposed method, Ordin…
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 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…
Calibrated Uncertainty for Molecular Property Prediction using Ensembles of Message Passing Neural Networks
Jonas Busk, Peter Bjørn Jørgensen, Arghya Bhowmik +3
Data-driven methods based on machine learning have the potential to accelerate computational analysis of atomic structures. In this context, reliable uncertainty estimates are impo…