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Found 8 papers, 2 papers with code
Flexible and efficient spatial extremes emulation via variational autoencoders
Many real-world processes have complex tail dependence structures that cannot be characterized using classical Gaussian processes.
Statistical Deep Learning for Spatial and Spatio-Temporal Data
Deep neural network models have become ubiquitous in recent years, and have been applied to nearly all areas of science, engineering, and industry.
Bayesian Inverse Reinforcement Learning for Collective Animal Movement
Agent-based methods allow for defining simple rules that generate complex group behaviors.
Deep Integro-Difference Equation Models for Spatio-Temporal Forecasting
Both procedures tend to be excellent for prediction purposes over small time horizons, but are generally time-consuming and, crucially, do not provide a global prior model for the temporally-varying dynamics that is realistic.
Comparison of Deep Neural Networks and Deep Hierarchical Models for Spatio-Temporal Data
This overview paper presents a brief introduction to the deep hierarchical DSTM (DH-DSTM) framework, and deep models in machine learning, culminating with the deep neural DSTM (DN-DSTM).
Deep Echo State Networks with Uncertainty Quantification for Spatio-Temporal Forecasting
The methodology is first applied to a data set simulated from a novel non-Gaussian multiscale Lorenz-96 dynamical system simulation model and then to a long-lead United States (U. S.) soil moisture forecasting application.
Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data
Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used in the machine learning and dynamical systems literature to represent complex dynamical or sequential relationships between variables.
An Ensemble Quadratic Echo State Network for Nonlinear Spatio-Temporal Forecasting
Spatio-temporal data and processes are prevalent across a wide variety of scientific disciplines.
