Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction-Reference-Cited by-同舟云学术

Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:The World Wide Web Conference on - WWW '19
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Author:

Yao Huaxiu¹,Liu Yiding²,Wei Ying³,Tang Xianfeng¹,Li Zhenhui¹

Affiliation:

1. The Pennsylvania State University, USA

2. Nanyang Technological University, Singapore

3. Tencent, China

Publisher

ACM Press

Reference45 articles.

1. Wenyuan Dai, Ou Jin, Gui-Rong Xue, Qiang Yang, and Yong Yu. 2009. Eigentransfer: a unified framework for transfer learning. In Proceedings of the 26th Annual International Conference on Machine Learning. ACM, 193-200.

2. Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. In International Conference on Machine Learning. 1126-1135.

3. Boqing Gong, Yuan Shi, Fei Sha, and Kristen Grauman. 2012. Geodesic flow kernel for unsupervised domain adaptation. In CVPR. IEEE, 2066-2073.

4. Raghuraman Gopalan, Ruonan Li, and Rama Chellappa. 2011. Domain adaptation for object recognition: An unsupervised approach. In Computer Vision (ICCV), 2011 IEEE International Conference on. IEEE, 999-1006.

5. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735-1780.

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