Deephullnet: a deep learning approach for solving the convex hull and concave hull problems with transformer-Reference-Cited by-同舟云学术

Deephullnet: a deep learning approach for solving the convex hull and concave hull problems with transformer

Published:2024-05-28 Issue:1 Volume:17 Page:
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Liang Haojian¹²,Wang Shaohua¹³,Gao Song⁴,Li Huilai²⁵,Su Cheng¹,Lu Hao⁶,Zhang Xueyan⁷,Chen Xi⁸,Chen Yinan⁹

Affiliation:

1. Key Laboratory of Remote Sensing and Digital Earth Chinese Academy of Sciences, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of China

2. School of Artificial Intelligence, Jilin University, Changchun, People’s Republic of China

3. International Research Center of Big Data for Sustainable Development Goals, CAS, Beijing, People’s Republic of China

4. Department of Geography, University of Wisconsin-Madison, Madison, WI, USA

5. School of Mathematics, Jilin University, Changchun, People’s Republic of China

6. SuperMap Software Co., Ltd., Beijing, People’s Republic of China

7. Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA

8. The Bartlett Centre for Advanced Spatial Analysis, University College London, London, UK

9. School of Business Administration, South China University of Technology, Guangzhou, People’s Republic of China

Funder

National Key Research and Development Program of China

Talent Introduction Program Youth Project of the Chinese Academy of Sciences

the Hundred Talents Program Youth Project

Key Laboratory of Remote Sensing and Digital Earth Chinese Academy of Sciences

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2024.2358843

Reference52 articles.

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4. The quickhull algorithm for convex hulls

5. Bello I. H. Pham Q. V. Le M. Norouzi and S. Bengio. 2016. “Neural Combinatorial Optimization with Reinforcement Learning.” arXiv preprint arXiv:1611.09940.