14 papers · 1 filter
Topological Flow Matching
Kacper Wyrwal, Ä°smail Ä°lkan Ceylan, Alexander Tong
Flow matching is a powerful generative modeling framework, valued for its simplicity and strong empirical performance. However, its standard formulation treats signals on structure…
What are the Right Symmetries for Formal Theorem Proving?
Krzysztof Olejniczak, Radoslav Dimitrov, Xingyue Huang +3
Formal theorem provers based on large language models (LLMs) are highly sensitive to superficial variations in problem representation: semantically equivalent statements can exhibi…
HYPER: A Foundation Model for Inductive Link Prediction with Knowledge Hypergraphs
Xingyue Huang, Mikhail Galkin, Michael M. Bronstein +1
Inductive link prediction with knowledge hypergraphs is the task of predicting missing hyperedges involving completely novel entities (i.e., nodes unseen during training). Existing…
MacroGuide: Topological Guidance for Macrocycle Generation
Alicja Maksymiuk, Alexandre Duplessis, Michael Bronstein +3
Macrocycles are ring-shaped molecules that offer a promising alternative to small-molecule drugs due to their enhanced selectivity and binding affinity against difficult targets. D…
Curly Flow Matching for Learning Non-gradient Field Dynamics
Katarina PetroviÄ, 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…
Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models
Ben Finkelshtein, Ä°smail Ä°lkan 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…