4 papers
Curly Flow Matching for Learning Non-gradient Field Dynamics
Katarina Petrović, Lazar Atanackovic, Viggo Moro +5
Modeling the transport dynamics of natural processes from population-level observations is a ubiquitous problem in the natural sciences. Such models rely on key assumptions about t…
Bringing Graphs to the Table: Zero-shot Node Classification via Tabular Foundation Models
Adrian Hayler, Xingyue Huang, İsmail İlkan Ceylan +2
Graph foundation models (GFMs) have recently emerged as a promising paradigm for achieving broad generalization across various graph data. However, existing GFMs are often trained…
Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models
Ben Finkelshtein, İsmail İlkan Ceylan, Michael Bronstein +1
Graph machine learning architectures are typically tailored to specific tasks on specific datasets, which hinders their broader applicability. This has led to a new quest in graph…
How Expressive are Knowledge Graph Foundation Models?
Xingyue Huang, Pablo Barceló, Michael M. Bronstein +4
Knowledge Graph Foundation Models (KGFMs) are at the frontier for deep learning on knowledge graphs (KGs), as they can generalize to completely novel knowledge graphs with differen…