2.4k citations · 6k across the 21 of their papers we have counts for
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Decoupling Time and Space: A Temporally Conditioned Refinement for EEG Source Imaging
Marco Morik, Jesse Palarus, Carmen Vidaurre +2
Electroencephalography (EEG) offers millisecond temporal resolution, but inferring underlying neural sources is a severely ill-posed spatial inverse problem. While deep learning ha…
Insightful analysis of historical sources at scales beyond human capabilities using unsupervised Machine Learning and XAI
Oliver Eberle, Jochen Büttner, Hassan El-Hajj +3
Historical materials are abundant. Yet, piecing together how human knowledge has evolved and spread both diachronically and synchronically remains a challenge that can so far only…
So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems
J. Thorben Frank, Oliver T. Unke, Klaus-Robert Müller
The application of machine learning methods in quantum chemistry has enabled the study of numerous chemical phenomena, which are computationally intractable with traditional ab-ini…
Building and Interpreting Deep Similarity Models
Oliver Eberle, Jochen Büttner, Florian Kräutli +3
Many learning algorithms such as kernel machines, nearest neighbors, clustering, or anomaly detection, are based on the concept of 'distance' or 'similarity'. Before similarities a…