3 papers
cs.CV2026
Zero-Shot Test-Time Canonicalization using Out-of-Distribution Scoring
Dominik Lindner, Johann Schmidt, Tom Siegl +2
Pretrained vision models often misclassify inputs that are rotated, scaled, or sheared, even though these affine transformations leave the object class unchanged. Robustness is usu…
cs.LG2025
Informed, but Not Always Improved: Challenging the Benefit of Background Knowledge in GNNs
KutalmıŠCoÅkun, Ivo Kavisanczki, Amin Mirzaei +4
In complex and low-data domains such as biomedical research, incorporating background knowledge (BK) graphs, such as protein-protein interaction (PPI) networks, into graph-based ma…
cs.LG2025
SubROC: AUC-Based Discovery of Exceptional Subgroup Performance for Binary Classifiers
Tom Siegl, KutalmıŠCoÅkun, Bjarne C. Hiller +3
Machine learning (ML) is increasingly employed in real-world applications like medicine or economics, thus, potentially affecting large populations. However, ML models often do not…