Dynamic depth-width optimization for capsule graph convolutional network

Shangwei WU , Yingtong XIONG , Chuliang WENG

Front. Comput. Sci. ›› 2023, Vol. 17 ›› Issue (6) : 176346

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Front. Comput. Sci. ›› 2023, Vol. 17 ›› Issue (6) : 176346 DOI: 10.1007/s11704-023-2483-4
Artificial Intelligence
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Dynamic depth-width optimization for capsule graph convolutional network

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Shangwei WU, Yingtong XIONG, Chuliang WENG. Dynamic depth-width optimization for capsule graph convolutional network. Front. Comput. Sci., 2023, 17(6): 176346 DOI:10.1007/s11704-023-2483-4

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References

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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

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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

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

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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

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

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