3 papers
cs.LG2024
Pullback Flow Matching on Data Manifolds
Friso de Kruiff, Erik Bekkers, Ozan Öktem +2
We propose Pullback Flow Matching (PFM), a novel framework for generative modeling on data manifolds. Unlike existing methods that assume or learn restrictive closed-form manifold…
cs.LG2024
Score-based Pullback Riemannian Geometry: Extracting the Data Manifold Geometry using Anisotropic Flows
Willem Diepeveen, Georgios Batzolis, Zakhar Shumaylov +1
Data-driven Riemannian geometry has emerged as a powerful tool for interpretable representation learning, offering improved efficiency in downstream tasks. Moving forward, it is cr…
q-bio.BM2023
Riemannian geometry for efficient analysis of protein dynamics data
Willem Diepeveen, Carlos Esteve-Yagüe, Jan Lellmann +2
An increasingly common viewpoint is that protein dynamics data sets reside in a non-linear subspace of low conformational energy. Ideal data analysis tools for such data sets shoul…