Machine Learning Research Group at University of California, San Diego - Spatiotemporal Machine Learning
In this paper we approach the forecasting task with an alternative technique -- spatiotemporal machine learning. We present COVID-LSTM, a data-driven model based on a Long Short-term Memory deep learning architecture for forecasting COVID-19 incidence at the county-level in the US. We use ...
也就是说,它们代表了一个动态系统。 使用gstat包实现时空克里格法(Spatio-Temporal Kriging) 在本实验中,我们将使用带有gstat包的半变异函数(semivariogram)来完成时空通用克里格法(Spatio-Temporal universal kriging)的过程。我们重点关注 1993 年 7 月 NOAA 数据集 (Tmax) 中的最高温度数据。 1. 加载所需要的包 ...
Learning Spatiotemporal Features with 3D Convolutional Networks 用3D卷积网络学习时空特征 摘要We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional …
Less is more: a new machine-learning methodology for spatiotemporal systems Machine learning provides a way to use only portions of the variables of a spatiotemporal system to predict its subsequent evolution and consequently avoid... S Feng,K Wang,F Wang,... - 理论物理通讯:英文版 被引量:...
Research in action detection has grown in the recent years, as it plays a key role in video understanding. Modelling the interactions (either spatial or temporal) between actors and their context has proven to be essential for this task. While recent works use spatial features with aggregated te...
et al. PDEBench: an extensive benchmark for scientific machine learning. Adv. Neural Inf. Process. Syst. 35, 1596–1611 (2022). Google Scholar Li, Z. S3GM: learning spatiotemporal dynamics with a pretrained generative model. Zenodo https://doi.org/10.5281/zenodo.13925732 (2024). Li, Z...
temporal pyramid matching (Lazebnik et al.2006) is used to generate a kernel for each visual word. Football plays are then classified into seven categories using multiple kernel learning. Hervieu and Bouthemy (2010) use a hierarchical parallel semi-Markov model to classify different activity ...
Importance of target-oriented validation strategies for spatio-temporal prediction models is illustrated using two case studies: (1) modelling of air temperature ( T air T a i r mathContainer Loading Mathjax ) in Antarctica, and (2) modelling of volumetric water content (VW) for the R.J. ...
We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised video dataset. Our findings are three-fold: 1) 3D ConvNets are more suitable for spatiotemporal feature learning compared...