Quantifying Uncertainty in Deep Spatiotemporal Forecasting-Reference-Cited by-同舟云学术

Quantifying Uncertainty in Deep Spatiotemporal Forecasting

Published:2021-08-14 Issue: Volume: Page:
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Container-title:Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
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Author:

Wu Dongxia¹,Gao Liyao²,Chinazzi Matteo³,Xiong Xinyue³,Vespignani Alessandro³,Ma Yi-An¹,Yu Rose¹

Affiliation:

1. University of California, San Diego, La Jolla, CA, USA

2. University of Washington, Seattle, WA, USA

3. Northeastern University, Boston, MA, USA

Funder

Google Faculty Award

HHS/CDC

AWS Machine Learning Research Award

DARPA

Google Cloud Research Credits program

NSF Grant

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3447548.3467325

Reference70 articles.

1. Stochastic Gradient MCMC for State Space Models

2. AM Alaa and M van der Schaar. 2020. Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions. In ICML . AM Alaa and M van der Schaar. 2020. Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions. In ICML .

3. On the Comparison of Interval Forecasts

4. Multiscale mobility networks and the spatial spreading of infectious diseases

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