4 papers
Can Computational Reducibility Lead to Transferable Models for Graph Combinatorial Optimization?
Semih Cantürk, Semih Cantürk, Thomas Sabourin +3
A key challenge in developing unified neural solvers for combinatorial optimization (CO) is the efficient generalization of models from a given set of tasks to new tasks unseen dur…
Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings
Billy Joe Franks, Moshe Eliasof, Semih Cantürk +4
Recent advances in integrating positional and structural encodings (PSEs) into graph neural networks (GNNs) have significantly enhanced their performance across various graph learn…
OpenQDC: Open Quantum Data Commons
Cristian Gabellini, Nikhil Shenoy, Stephan Thaler +5
Machine Learning Interatomic Potentials (MLIPs) are a highly promising alternative to force-fields for molecular dynamics (MD) simulations, offering precise and rapid energy and fo…
Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNs
Frederik Wenkel, Semih Cantürk, Stefan Horoi +2
Graph neural networks (GNNs) have achieved great success for a variety of tasks such as node classification, graph classification, and link prediction. However, the use of GNNs (an…