3 citations · 13 across the 27 of their papers we have counts for
17 papers · 1 filter
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…
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…
Crystalite: A Lightweight Transformer for Efficient Crystal Modeling
Tin Hadži Veljković, Joshua Rosenthal, Ivor Lončarić +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…
Pawsterior: Variational Flow Matching for Structured Simulation-Based Inference
Jorge Carrasco-Pollo, Floor Eijkelboom, Jan-Willem van de Meent
We introduce Pawsterior, a variational flow-matching framework for improved and extended simulation-based inference (SBI). Many SBI problems involve posteriors constrained by struc…
CORDS: Continuous Representations of Discrete Structures
Tin Hadži Veljković, Erik Bekkers, Michael Tiemann +1
Many learning problems require predicting sets of objects when the number of objects is not known beforehand. Examples include object detection, molecular modeling, and scientific…
MSPT: Efficient Large-Scale Physical Modeling via Parallelized Multi-Scale Attention
Pedro M. P. Curvo, Jan-Willem van de Meent, Maksim Zhdanov
A key scalability challenge in neural solvers for industrial-scale physics simulations is efficiently capturing both fine-grained local interactions and long-range global dependenc…