STORM-GAN+: spatio-temporal meta-GAN for cross-city estimation of heterogeneous human mobility responses to COVID-19
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Hardware and Architecture,Human-Computer Interaction,Information Systems,Software
Link
https://link.springer.com/content/pdf/10.1007/s10115-023-01921-7.pdf
Reference47 articles.
1. Kraemer MU, Yang C-H, Gutierrez B, Wu C-H, Klein B, Pigott DM, Du Plessis L, Faria NR, Li R, Hanage WP et al (2020) The effect of human mobility and control measures on the covid-19 epidemic in china. Science 368(6490):493–497
2. Bao H, Zhou X, Zhang Y, Li Y, Xie Y (2020) Covid-gan: estimating human mobility responses to covid-19 pandemic through spatio-temporal conditional generative adversarial networks. In: Proceedings of the 28th international conference on advances in geographic information systems. SIGSPATIAL ’20, pp 273–282. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3397536.3422261
3. Liang W, Liu Z, Liu C (2020) Dawson: A domain adaptive few shot generation framework. arXiv preprint arXiv:2001.00576
4. Yao H, Liu Y, Wei Y, Tang X, Li Z (2019) Learning from multiple cities: a meta-learning approach for spatial-temporal prediction. In: The World Wide Web conference, pp 2181–2191
5. Zhang Y, Li Y, Zhou X, Luo J (2020) cst-ml: Continuous spatial-temporal meta-learning for traffic dynamics prediction. In: 2020 IEEE international conference on data mining (ICDM), pp 1418–1423. IEEE
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