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Statistical Deep Learning for Spatial and Spatio-Temporal Data

Statistical Deep Learning for Spatial and Spatio-Temporal Data

5 June 2022
C. Wikle
A. Zammit‐Mangion
    BDL
ArXivPDFHTML

Papers citing "Statistical Deep Learning for Spatial and Spatio-Temporal Data"

6 / 6 papers shown
Title
Neural Spatiotemporal Point Processes: Trends and Challenges
Neural Spatiotemporal Point Processes: Trends and Challenges
Sumantrak Mukherjee
Mouad Elhamdi
George Mohler
David Selby
Yao Xie
Sebastian Vollmer
Gerrit Grossmann
AI4TS
125
1
0
13 Feb 2025
A New Class of Realistic Spatio-Temporal Processes with Advection and
  Their Simulation
A New Class of Realistic Spatio-Temporal Processes with Advection and Their Simulation
Maria Laura Battagliola
S. Olhede
30
0
0
05 Mar 2023
ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics
  Forecasting
ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecasting
Yu Huang
James Li
Min Shi
H. Zhuang
Xingquan Zhu
Laurent Chérubin
James H. VanZwieten
Yufei Tang
AI4CE
PINN
23
6
0
12 Aug 2021
Convolutional LSTM Network: A Machine Learning Approach for
  Precipitation Nowcasting
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
Xingjian Shi
Zhourong Chen
Hao Wang
Dit-Yan Yeung
W. Wong
W. Woo
201
7,884
0
13 Jun 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
247
9,109
0
06 Jun 2015
Manifold Gaussian Processes for Regression
Manifold Gaussian Processes for Regression
Roberto Calandra
Jan Peters
C. Rasmussen
M. Deisenroth
84
271
0
24 Feb 2014
1