Author

A. E. J. Wedenborg

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#machine learning Preprint Oct 2026

Efficient and Generalizable Archetypal Analysis for Discrete Data

Archetypal Analysis (AA) represents observations as convex combinations of extremal data-driven profiles, yielding interpretable low-dimensional descriptions of complex datasets. Classical AA relies on a least-squares objective, which is poorly suited to discrete observations such as binary, count, and categorical data...

A. E. J. Wedenborg, Jesper L. Hinrich, Morten Mørup · 0 citations

#artificial intelligence Preprint Oct 2026

The Polytopal Neural Network

Understanding how deep neural networks process information remains a central challenge. Existing interpretability methods often compromise structural fidelity, rely on prespecified corpora, or explain models post-hoc. We propose Polytopal Neural Networks (PNNs), a framework that extracts distinct layer-wise aspects by...

A. E. J. Wedenborg, A. V. Nørskov, Teresa Dorszewski et al. · 0 citations