[1] Ireland, D., et al. (2022). “LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation.” arXiv preprint arXiv:2205.10106.
[2] Kim, M., et al. (2021). “Learning collaborative policies to solve NP-hard routing problems.” Advances in Neural Information Processing Systems, 34, 10418-10430.
[3] Kool, W., et al. (2018). “Attention, learn to solve routing problems!.” arXiv preprint arXiv:1803.08475.
[4] Kwon, Y., et al. (2020). “Pomo: Policy optimization with multiple optima for reinforcement learning.” Advances in Neural Information Processing Systems, 33, 21188-21198.
[5] He, Y., et al. (2020). “Circuit routing using monte carlo tree search and deep neural networks.” arXiv preprint arXiv:2006.13607.
[6] Leitner, M. et al. (2014). “New real-world instances for the Steiner tree problem in graphs.” ISOR, Uni Wien, Tech. Rep.
[7] Hamilton, W., et al. (2017). “Inductive representation learning on large graphs.” Advances in neural information
[8] Cangea, C., et al. (2018). “Towards sparse hierarchical graph classifiers.” arXiv preprint arXiv:1811.01287.
[9] Oord, A. V. D., et al. (2018). “Representation learning with contrastive predictive coding.” arXiv preprint arXiv:1807.03748.
[10] Manchanda, S., et al. (2020). “Gcomb: Learning budget-constrained combinatorial algorithms over billion-sized graphs.” Advances in Neural Information Processing Systems, 33, 20000-20011.
[11] Duan, H., et al. (2022). “Augment with Care: Contrastive Learning for Combinatorial Problems.” In International Conference on Machine Learning (pp. 5627-5642). PMLR.
[12] Selsam, D., et al. (2018). “Learning a SAT solver from single-bit supervision.” arXiv preprint arXiv:1802.03685.
[13] Chen, T., et al. (2020). “A simple framework for contrastive learning of visual representations.” In International conference on machine learning (pp. 1597-1607). PMLR.
