6 papers
Uncertainty-aware Machine Learning Interatomic Potentials via Learned Functional Perturbations
Olga Zaghen, Maksim Zhdanov, Dario Coscia +2
Machine Learning Interatomic Potentials (MLIPs) achieve near ab initio accuracy at a fraction of the cost of quantum-mechanical simulations, yet they remain prone to silent failure…
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…
Graph Homomorphism Distortion: A Metric to Distinguish Them All and in the Latent Space Bind Them
Martin Carrasco, Olga Zaghen, Kavir Sumaraj +2
A large driver of the complexity of graph learning is the interplay between structure and features. When analyzing the expressivity of graph neural networks, however, existing appr…
Hypergraph Neural Networks through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Design
Lev Telyatnikov, Maria Sofia Bucarelli, Guillermo Bernardez +3
Most of the current hypergraph learning methodologies and benchmarking datasets in the hypergraph realm are obtained by lifting procedures from their graph analogs, leading to over…
Revisiting Random Walks for Learning on Graphs
Jinwoo Kim, Olga Zaghen, Ayhan Suleymanzade +2
We revisit a simple model class for machine learning on graphs, where a random walk on a graph produces a machine-readable record, and this record is processed by a deep neural net…
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
Mustafa Hajij, Mathilde Papillon, Florian Frantzen +40
We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: h…