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Geometric deep learning: progress, applications and challenges

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant Nos. 61771322, 61871186, 61971290) and Fundamental Research Foundation of Shenzhen (Grant No. JCYJ20190808160815125).

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Authors and Affiliations

  1. College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, China

    Wenming Cao, Canta Zheng, Zhiyue Yan & Weixin Xie

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  1. Wenming Cao
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  2. Canta Zheng
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Correspondence to Weixin Xie.

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Appendixes A and B. The supporting information is available online at info.scichina.com and link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.

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Cao, W., Zheng, C., Yan, Z. et al. Geometric deep learning: progress, applications and challenges. Sci. China Inf. Sci. 65, 126101 (2022). https://doi.org/10.1007/s11432-020-3210-2

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