15 papers
Crystalite: A Lightweight Transformer for Efficient Crystal Modeling
Tin Hadži VeljkoviÄ, Joshua Rosenthal, Ivor LonÄariÄ +1
Generative models for crystalline materials often rely on equivariant graph neural networks, which capture geometric structure well but are costly to train and slow to sample. We p…
Discovering Symmetry Groups with Flow Matching
Yuxuan Chen, Jung Yeon Park, Floor Eijkelboom +4
Symmetry is fundamental to understanding physical systems and can improve performance and sample efficiency in machine learning. Both pursuits require knowledge of the underlying s…
Follow the Mean: Reference-Guided Flow Matching
Pedro M. P. Curvo, Maksim Zhdanov, Floor Eijkelboom +1
Existing approaches to controllable generation typically rely on fine-tuning, auxiliary networks, or test-time search. We show that flow matching admits a different control interfa…
(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…
Kernel-Gradient Drifting Models
Maria Esteban-Casadevall, Jorge Carrasco-Pollo, Max Welling +3
We propose kernel-gradient drifting, a one-step generative modeling framework that replaces the fixed Euclidean displacement direction in drifting models with directions induced by…
Purrception: Variational Flow Matching for Vector-Quantized Image Generation
RÄzvan-Andrei MatiÅan, Vincent Tao Hu, Grigory Bartosh +6
We introduce Purrception, a variational flow matching approach for vector-quantized image generation that provides explicit categorical supervision while maintaining continuous tra…