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Jul 23, 2020 · Materials and Methods: We proposed a spatio-temporal attention network (STAN) for pandemic prediction. It uses an attention-based graph convolutional network to capture geographical and temporal trends and predict the number of cases for a fixed number of days into the future.
- Junyi Gao, Rakshith Sharma, Cheng Qian, Lucas M Glass, Jeffrey Spaeder, Justin Romberg, Jimeng Sun, ...
- 2021
Jan 22, 2021 · We proposed a spatio-temporal attention network (STAN) for pandemic prediction. It uses a graph attention network to capture spatio-temporal trends of disease dynamics and to predict the number of cases for a fixed number of days into the future. We also designed a dynamics-based loss term for enhancing long-term predictions.
- Junyi Gao, Rakshith Sharma, Cheng Qian, Lucas M Glass, Jeffrey Spaeder, Justin Romberg, Jimeng Sun, ...
- 2021
STAN: spatio-temporal attention network for pandemic prediction using real-world evidence. J Gao, R Sharma, C Qian, LM Glass, J Spaeder, J Romberg, J Sun, ... Journal of the American Medical...
STAN: spatio-temporal attention network for pandemic prediction using real-world evidence. Junyi Gao, Rakshith Sharma, Cheng Qian, Lucas M Glass, Jeffrey Spaeder, Justin Romberg, Jimeng Sun, Cao Xiao. Research output: Contribution to journal › Article › peer-review.
Mar 18, 2021 · Materials and methods: We proposed a spatio-temporal attention network (STAN) for pandemic prediction. It uses a graph attention network to capture spatio-temporal trends of disease dynamics and to predict the number of cases for a fixed number of days into the future.
- Junyi Gao, Rakshith Sharma, Cheng Qian, Lucas M Glass, Jeffrey Spaeder, Justin Romberg, Jimeng Sun, ...
- 2021
Jul 23, 2020 · We focus on the STAN model developed by Gao et al. (2021), a spatio-temporal GAT developed to predict COVID-19 progression based on Johns Hopkins and patient claims data.
Jun 9, 2022 · SimVP: Simpler yet Better Video Prediction. Zhangyang Gao, Cheng Tan, Lirong Wu, Stan Z. Li. From CNN, RNN, to ViT, we have witnessed remarkable advancements in video prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training strategies.
