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Dynamic depth-width optimization for capsule graph convolutional network

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References

  1. Abadal S, Jain A, Guirado R, López-Alonso J, Alarcón E. Computing graph neural networks: a survey from algorithms to accelerators. ACM Computing Surveys, 2022, 54(9): 191

    Article  Google Scholar 

  2. Zhang M H, Cui Z C, Neumann M, Chen Y X. An end-to-end deep learning architecture for graph classification. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence and 30th Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence. 2018, 544

  3. Verma S, Zhang Z L. Graph capsule convolutional neural networks. 2018, arXiv preprint arXiv: 1805.08090

  4. Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017, 3859–3869

  5. Zhang X Y, Chen L H. Capsule graph neural network. In: Proceedings of International Conference on Learning Representations. 2019

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62141214 and 62272171).

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Correspondence to Chuliang Weng.

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The supporting information is available online at journa.hep.com.cn and link.springer.com.

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Wu, S., Xiong, Y. & Weng, C. Dynamic depth-width optimization for capsule graph convolutional network. Front. Comput. Sci. 17, 176346 (2023). https://doi.org/10.1007/s11704-023-2483-4

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  • DOI: https://doi.org/10.1007/s11704-023-2483-4

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