Graph Neural Processes for Spatio-Temporal Extrapolation-Reference-Cited by-同舟云学术

Graph Neural Processes for Spatio-Temporal Extrapolation

Published:2023-08-04 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
language:
Short-container-title:

Author:

Hu Junfeng¹^ORCID,Liang Yuxuan²^ORCID,Fan Zhencheng³^ORCID,Chen Hongyang⁴^ORCID,Zheng Yu⁵^ORCID,Zimmermann Roger¹^ORCID

Affiliation:

1. National University of Singapore, Singapore, Singapore

2. Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

3. University of Technology Sydney, Sydney, NSW, Australia

4. Zhejiang Lab, Hangzhou, China

5. JD Intelligent Cities Research & JD iCity, JD Technology, Beijing, China

Funder

Singapore Ministry of Education Academic Research Fund Tier 2

Guangzhou Municipal Science and Technology Project

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599372

Reference51 articles.

1. Emre Aksan and Otmar Hilliges . 2019 . STCN: Stochastic Temporal Convolutional Networks. In International Conference on Learning Representations, ICLR. Emre Aksan and Otmar Hilliges. 2019. STCN: Stochastic Temporal Convolutional Networks. In International Conference on Learning Representations, ICLR.

2. Kriging Convolutional Networks

3. Christopher M Bishop and Nasser M Nasrabadi . 2006. Pattern recognition and machine learning . Springer . Christopher M Bishop and Nasser M Nasrabadi. 2006. Pattern recognition and machine learning. Springer.

4. Bike flow prediction with multi-graph convolutional networks

5. Weiyu Cheng Yanyan Shen Yanmin Zhu and Linpeng Huang. 2018. A neural attention model for urban air quality inference: Learning the weights of monitoring stations. In AAAI. 2151--2158. Weiyu Cheng Yanyan Shen Yanmin Zhu and Linpeng Huang. 2018. A neural attention model for urban air quality inference: Learning the weights of monitoring stations. In AAAI. 2151--2158.