PCA identifies the main factors of variation in a dataset, but it is limited to linear relationships. Normalizing flows can capture more complex patterns, but they lack interpretability.
EOFlows aim to combine the flexibility of normalizing flows with the interpretability of PCA. Using orthogonality regularization, they learn latent dimensions that capture distinct factors of variation, which can be ranked by their explained entropy.
In this talk, Prof. Köthe will explain how EOFlows work and show what they discover in the CelebA portrait dataset. He will also discuss how stable the results are across training runs and how the method compares with existing approaches.

The use of catalysts in an excited state enables a range of chemical transformations that are not accessible by conventional methods. Photoexcitation generates strongly oxidising and reducing agents, which can serve as valuable alternatives to hazardous or costly reagents. Flavins and deazaflavins, prominent natural chromophores (e.g. riboflavin), are characterised by several stable redox and excited states. Consequently, flavin-based photocatalysts are well suited to drive various redox reactions. Recent results from our group and others have shown that flavin species can act as reducing agents, with reducing capabilities comparable to sodium.
In this presentation, the rational design of flavin- and deazaflavin-based systems for photoreductions will be discussed. These systems are based either on a conPET process involving excited flavin radical anions, anionic flavin species and solvated electrons. Applications will be demonstrated, including the generation of phenyl radicals from aryl fluorides, chlorides and bromides, followed by coupling reactions, as well as reductive detriflylation and detosylation. Reductive power of flavin radicals also allows flavin regeneration via solvent (acetonitrile) reduction
in oxidative processes.

Lab presentations
Lena Maier-Hein • Claudia Strauch • Alexander Zipf

Rocket science
Leon Mayer (Maier-Hein Lab)
How Naturally Adverbial Medical Images Challenge Vision-Language Models

Marta Gentiloni Silveri (Strauch Lab)
How Reliably Can Generative Models Reconstruct Data from Pure Noise?

Gefei Kong (Zipf Lab)
From bird’s eyes to human eyes: mapping street-view greenness by DL model from satellite imagery and geo foundation embeddings