🏢 Tübingen AI Center

Spectral distances on k-nearest neighbor graphs enable robust topological analysis of high-dimensional noisy data using persistent homology, overcoming limitations of Euclidean distance.

New scalable method measures per-unit interpretability in vision DNNs without human evaluation, revealing anti-correlation between model performance and interpretability.