Author

Tom Siegl

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#artificial intelligence Sep 2025

Geometrically Constrained and Token-Based Probabilistic Spatial Transformers

A novel STN framework that decomposes affine transformations into interpretable primitives, regressed under adaptable geometric constraints, thereby preventing the training instability typically caused by degenerate transformations.

Johann Schmidt, Tom Siegl, Martin Becker et al. · arXiv.org · 3 citations

Preprint Aug 2026

CytoBERT: A Foundation Model for Cytometry Data

Fine-tuning CytoBERT for sample-level classification demonstrates that transfer learning across heterogeneous cytometry datasets is feasible, providing a starting point for scalable, generalizable cytometry analysis.

S. A. Qadri, Bjarne C. Hiller, F. Blanke et al. · 0 citations