4 papers
Mol-JEPA: A multimodal Joint Embedding Predictive Architecture for Molecules
Florian Rottach, Sebastian Schieferdecker, William Rudman +2
Despite recent advances in molecular foundation models, several limitations remain, such as chemically invalid augmentations, modality collapse, and incomplete representation of bi…
Isotropy Cliffs: The Geometric Signature of Decision-Making in Large Language Models
Okan S. Coskun, Florian Rottach, Carsten Eickhoff +1
We investigate the geometry of decision-making in Multiple Choice Question Answering (MCQA) through the lens of isotropy. Analyzing five open-weight models across diverse datasets,…
From Topology to Retrieval: Decoding Embedding Spaces with Unified Signatures
Florian Rottach, William Rudman, Bastian Rieck +2
Studying the shape of text embedding spaces enhances our understanding of model behavior and how geometry affects downstream task performance. While a range of topological and geom…
Molecular Machine Learning Using Euler Characteristic Transforms
Victor Toscano-Duran, Florian Rottach, Bastian Rieck
The shape of a molecule determines its physicochemical and biological properties. However, it is often underrepresented in standard molecular representation learning approaches. He…