Graph Energy Matching: A Transport-Aligned Energy-Based Model for Graph Generation
By
† equal advising.
Summary
This preprint introduces Graph Energy Matching (GEM), a novel generative modeling approach for discrete graph-structured data. GEM combines transport toward the data manifold with local energy-guided mixing, addressing challenges in molecular discovery and materials design. The method is developed by researchers from the University of Zurich, Harvard University, and the Kempner Institute.
Source
Twitter / XGraph Energy Matching: A Transport-Aligned Energy-Based Model for Graph Generationmichalbalcerak.aiKey quotes
· 2 pulledGenerative modeling of discrete data, such as graphs, underpins many scientific and industrial applications, including molecular discovery and materials design.
GEM sampling overview: transport toward the data manifold followed by local energy-guided mixing.
You might also wanna read
Reconstruction of Graph Signals on Complex Manifolds With Kernel Methods
The Bulletin: The raw energy of collisions, crossovers, and reinventions [Issue #122]
On the robustness of topological gap detection via transport
Mathematical Unification of Decision Trees and Diffusion Models via Global Trajectory Score Matching
This paper establishes a mathematical correspondence between decision trees and diffusion models, unifying these two seemingly disparate mod
arxiv.org·1mo ago
Hungarian Researchers Develop New Method for Solving Quantum Chemistry Problems
Hungarian researchers Örs Legeza (HUN-REN Wigner Research Centre for Physics) and Andor Menczer (ELTE) have developed a new computational me
hungarytoday.hu·1mo ago
MpGEMM: Optimizing General Matrix Multiplication for ARM's Scalable Matrix Extension Architecture
This research paper presents MpGEMM, an open-source library that optimizes General Matrix Multiplication (GEMM) for ARM's Scalable Matrix Ex
arxiv.org·5mo ago
Comments
Sign in to join the conversation.
No comments yet. Be the first.