Combinatorial optimization with physics-inspired graph neural networks MJA Schuetz, JK Brubaker, HG Katzgraber Nature Machine Intelligence 4 (4), 367-377, 2022 | 199 | 2022 |
Graph coloring with physics-inspired graph neural networks MJA Schuetz, JK Brubaker, Z Zhu, HG Katzgraber Physical Review Research 4 (4), 043131, 2022 | 43 | 2022 |
Optimization of robot-trajectory planning with nature-inspired and hybrid quantum algorithms MJA Schuetz, JK Brubaker, H Montagu, Y van Dijk, J Klepsch, P Ross, ... Physical Review Applied 18 (5), 054045, 2022 | 23 | 2022 |
Designing quantum annealing schedules using Bayesian optimization JR Finžgar, MJA Schuetz, JK Brubaker, H Nishimori, HG Katzgraber Physical Review Research 6 (2), 023063, 2024 | 20 | 2024 |
Reply to: Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent set MJA Schuetz, JK Brubaker, HG Katzgraber Nature Machine Intelligence 5 (1), 32-34, 2023 | 10 | 2023 |
Explainable Artificial Intelligence Using Expressive Boolean Formulas G Rosenberg, JK Brubaker, MJA Schuetz, G Salton, Z Zhu, EY Zhu, ... Machine Learning and Knowledge Extraction 5 (4), 1760-1795, 2023 | 8 | 2023 |
Reply to: Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems MJA Schuetz, JK Brubaker, HG Katzgraber Nature Machine Intelligence 5 (1), 26-28, 2023 | 2 | 2023 |
Quadratic unconstrained binary optimization and constraint programming approaches for lattice-based cyclic peptide docking JK Brubaker, KEC Booth, A Arakawa, F Furrer, J Ghosh, T Sato, ... arXiv preprint arXiv:2412.10260, 2024 | | 2024 |
Scalable iterative pruning of large language and vision models using block coordinate descent G Rosenberg, JK Brubaker, MJA Schuetz, EY Zhu, S Kadıoğlu, ... arXiv preprint arXiv:2411.17796, 2024 | | 2024 |