1 citations · 1 across the 15 of their papers we have counts for
12 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…
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…
Categorical Flow Maps
Daan Roos, Oscar Davis, Floor Eijkelboom +5
We introduce Categorical Flow Maps, a flow-matching method for accelerated few-step generation of categorical data via self-distillation. Building on recent variational formulation…
Flow Matching for Tabular Data Synthesis
Bahrul Ilmi Nasution, Floor Eijkelboom, Mark Elliot +2
Synthetic data generation is an important tool for privacy-preserving data sharing. Although diffusion models have set recent benchmarks, flow matching (FM) offers a promising alte…
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…
Exponential Family Variational Flow Matching for Tabular Data Generation
Andrés Guzmán-Cordero, Floor Eijkelboom, Jan-Willem van de Meent
While denoising diffusion and flow matching have driven major advances in generative modeling, their application to tabular data remains limited, despite its ubiquity in real-world…
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…
E(n) Equivariant Message Passing Cellular Networks
Veljko Kovač, Erik J. Bekkers, Pietro Liò +1
This paper introduces E(n) Equivariant Message Passing Cellular Networks (EMPCNs), an extension of E(n) Equivariant Graph Neural Networks to CW-complexes. Our approach addresses tw…
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…
Can strong structural encoding reduce the importance of Message Passing?
Floor Eijkelboom, Erik Bekkers, Michael Bronstein +1
The most prevalent class of neural networks operating on graphs are message passing neural networks (MPNNs), in which the representation of a node is updated iteratively by aggrega…
E(n) Equivariant Message Passing Simplicial Networks
Floor Eijkelboom, Rob Hesselink, Erik Bekkers
This paper presents Equivariant Message Passing Simplicial Networks (EMPSNs), a novel approach to learning on geometric graphs and point clouds that is equivariant…