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cs.LG2026

(Sparse) Attention to the Details: Preserving Spectral Fidelity in ML-based Weather Forecasting Models

Maksim Zhdanov, Ana Lucic, Max Welling +1

We introduce Mosaic, a probabilistic weather forecasting model that addresses three failure modes of spectral degradation in ML-based weather prediction: spectral damping (statisti…

cs.LG2026

Riemannian Variational Flow Matching for Material and Protein Design

Olga Zaghen, Floor Eijkelboom, Alison Pouplin +4

We present Riemannian Gaussian Variational Flow Matching (RG-VFM), a geometric extension of Variational Flow Matching (VFM) for generative modeling on manifolds. Motivated by the b…

cs.LG2025

Controlled Generation with Equivariant Variational Flow Matching

Floor Eijkelboom, Heiko Zimmermann, Sharvaree Vadgama +4

We derive a controlled generation objective within the framework of Variational Flow Matching (VFM), which casts flow matching as a variational inference problem. We demonstrate th…

cs.LG2025

Variational Flow Matching for Graph Generation

Floor Eijkelboom, Grigory Bartosh, Christian Andersson Naesseth +2

We present a formulation of flow matching as variational inference, which we refer to as variational flow matching (VFM). Based on this formulation we develop CatFlow, a flow match…

cs.LG2025

Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systems

Maksim Zhdanov, Max Welling, Jan-Willem van de Meent

Large-scale physical systems defined on irregular grids pose significant scalability challenges for deep learning methods, especially in the presence of long-range interactions and…