208 citations · 968 across the 37 of their papers we have counts for
5 papers · 1 filter
Crystal structure prediction using graph neural combinatorial optimization
Stavros Gerolymatos, J. Kyle Brubaker, Martin J. A. Schuetz +1
Crystalline materials are widely used in technological applications, yet their discovery remains a significant challenge. As their properties are driven by structure, crystal struc…
Scalable iterative pruning of large language and vision models using block coordinate descent
Gili Rosenberg, J. Kyle Brubaker, Martin J. A. Schuetz +4
Pruning neural networks, which involves removing a fraction of their weights, can often maintain high accuracy while significantly reducing model complexity, at least up to a certa…
Reply to: Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems
Martin J. A. Schuetz, J. Kyle Brubaker, Helmut G. Katzgraber
We provide a comprehensive reply to the comment written by Stefan Boettcher [arXiv:2210.00623] and argue that the comment singles out one particular non-representative example prob…
Graph Coloring with Physics-Inspired Graph Neural Networks
Martin J. A. Schuetz, J. Kyle Brubaker, Zhihuai Zhu +1
We show how graph neural networks can be used to solve the canonical graph coloring problem. We frame graph coloring as a multi-class node classification problem and utilize an uns…
Combinatorial Optimization with Physics-Inspired Graph Neural Networks
Martin J. A. Schuetz, J. Kyle Brubaker, Helmut G. Katzgraber
Combinatorial optimization problems are pervasive across science and industry. Modern deep learning tools are poised to solve these problems at unprecedented scales, but a unifying…