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Recent works on machine learning for combinatorial optimization have shown that learning based approaches can outperform heuristic methods in terms of speed and performance.
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Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip, “A comprehensive survey on graph neural networks,” IEEE transactions on neural networks and learning systems , vol. 32, no. 1, pp. 4–24, 2020
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2018
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P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” in International Conference on Learning Representations , 2018. [Online]. Available: https://openreview.net/forum?id=rJXMpikCZ
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2019
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M. Zhang, S. Jiang, Z. Cui, R. Garnett, and Y. Chen, “D-VAE: A variational autoencoder for directed acyclic graphs,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
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B. H. Ahn, J. Lee, J. M. Lin, H.-P. Cheng, J. Hou, and H. Esmaeilzadeh, “Ordering chaos: Memory-aware scheduling of irregularly wired neural networks for edge devices,” Proceedings of Machine Learning and Systems , vol. 2, pp. 44–57, 2020
2020
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A. Paliwal, F. Gimeno, V. Nair, Y. Li, M. Lubin, P. Kohli, and O. Vinyals, “Reinforced genetic algorithm learning for optimizing computation graphs,” in International Conference on Learning Representations , 2020. [Online]. Available: https://openreview.net/forum?id=rkxDoJBYPB
2020
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Y. Zhou, S. Roy, A. Abdolrashidi, D. Wong, P. Ma, Q. Xu, H. Liu, P. Phothilimtha, S. Wang, A. Goldie et al. , “Transferable graph optimizers for ML compilers,” Advances in Neural Information Processing Systems , vol. 33, pp. 13 844–13 855, 2020
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A. Gadetsky, K. Struminsky, C. Robinson, N. Quadrianto, and D. Vetrov, “Low-variance black-box gradient estimates for the plackett-luce distribution,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 06, 2020, pp. 10 126–10 135
2020
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Y. Bengio, A. Lodi, and A. Prouvost, “Machine learning for combinatorial optimization: a methodological tour d’horizon,” European Journal of Operational Research , vol. 290, no. 2, pp. 405–421, 2021
2021
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L. Xin, W. Song, Z. Cao, and J. Zhang, “NeuroLKH: Combining deep learning model with lin-kernighan-helsgaun heuristic for solving the traveling salesman problem,” in Advances in Neural Information Processing Systems , 2021. [Online]. Available: https://openreview.net/forum?id=VKVShLsAuZ
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2021
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C. Ying, T. Cai, S. Luo, S. Zheng, G. Ke, D. He, Y. Shen, and T.-Y. Liu, “Do transformers really perform badly for graph representation?” Advances in Neural Information Processing Systems , vol. 34, 2021
2021
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R. Wang, Z. Hua, G. Liu, J. Zhang, J. Yan, F. Qi, S. Yang, J. ZHOU, and X. Yang, “A bi-level framework for learning to solve combinatorial optimization on graphs,” in Advances in Neural Information Processing Systems , 2021
2021
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A. Correia, D. E. Worrall, and R. Bondesan, “Neural simulated annealing,” 2022. [Online]. Available: https://openreview.net/forum?id=bHqI0DvSIId
2022
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