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REDS: Random ensemble deep spatial prediction
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/ecy.1674openalex-snapshot
The basis function approach for modeling autocorrelation in ecological data
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Deep echo state networks with uncertainty quantification for spatio‐temporal forecasting
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An ensemble quadratic echo state network for non‐linear spatio‐temporal forecasting
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On nearest‐neighbor Gaussian process models for massive spatial data
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Computational Neural Networks — Tools for Spatial Data Analysis
inferredcites →https://doi.org/10.1007/978-1-4419-9096-9openalex-snapshot
Reproducing Kernel Hilbert Spaces in Probability and Statistics
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Estimation of Disease Rates in Small Areas: A new Mixed Model for Spatial Dependence
inferredcites →https://doi.org/10.1007/978-1-4612-4380-9_41openalex-snapshot
Bootstrap Methods: Another Look at the Jackknife
inferredcites →https://doi.org/10.1007/978-1-4612-4380-9_6openalex-snapshot
Statistical Methods for Research Workers
inferredcites →https://doi.org/10.1007/978-3-540-74690-4_48openalex-snapshot
The Introduction of Time-Scales in Reservoir Computing, Applied to Isolated Digits Recognition
inferredcites →https://doi.org/10.1007/s13253-018-00348-wopenalex-snapshot
A Case Study Competition Among Methods for Analyzing Large Spatial Data
inferredcites →https://doi.org/10.1007/s13253-020-00401-7openalex-snapshot
Vecchia Approximations of Gaussian-Process Predictions
inferredcites →https://doi.org/10.1016/0925-2312(93)90006-oopenalex-snapshot
Backpropagation and stochastic gradient descent method
inferredcites →https://doi.org/10.1016/0925-2312(94)90053-1openalex-snapshot
Learning and generalization characteristics of the random vector functional-link net
inferredcites →https://doi.org/10.1016/j.asoc.2021.107239openalex-snapshot
On the origins of randomization-based feedforward neural networks
inferredcites →https://doi.org/10.1016/j.cageo.2013.10.008openalex-snapshot
Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information
inferredcites →https://doi.org/10.1016/j.csda.2021.107203openalex-snapshot
Deep distribution regression
inferredcites →https://doi.org/10.1016/j.epidem.2019.100362openalex-snapshot
Spatial analysis made easy with linear regression and kernels
inferredcites →https://doi.org/10.1016/j.inffus.2021.05.008openalex-snapshot
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
inferredcites →https://doi.org/10.1016/j.neucom.2005.12.126openalex-snapshot
Extreme learning machine: Theory and applications
inferredcites →https://doi.org/10.1016/j.spasta.2013.06.004openalex-snapshot
Scaling intrinsic Gaussian Markov random field priors in spatial modelling
inferredcites →https://doi.org/10.1016/j.spasta.2022.100598openalex-snapshot
Bayesian spatial modeling using random Fourier frequencies
inferredcites →https://doi.org/10.1023/a:1009666805688openalex-snapshot
A process-convolution approach to modelling temperatures in the North Atlantic Ocean
inferredcites →https://doi.org/10.1080/00401706.1989.10488504openalex-snapshot
Artificial Intelligence and Statistics
inferredcites →https://doi.org/10.1080/10618600.2014.914946openalex-snapshot
A Multiresolution Gaussian Process Model for the Analysis of Large Spatial Datasets
inferredcites →https://doi.org/10.1093/biomet/86.4.815openalex-snapshot
A dimension-reduced approach to space-time Kalman filtering
inferredcites →https://doi.org/10.1093/comjnl/14.4.422openalex-snapshot
An algorithm with guaranteed convergence for finding a zero of a function
inferredcites →https://doi.org/10.1109/cvprw50498.2020.00359openalex-snapshot
Randaugment: Practical automated data augmentation with a reduced search space
inferredcites →https://doi.org/10.1109/icpr.1992.201708openalex-snapshot
Feedforward neural networks with random weights
inferredcites →https://doi.org/10.1109/tkde.2016.2606428openalex-snapshot
Towards Bayesian Deep Learning: A Framework and Some Existing Methods
inferredcites →https://doi.org/10.1109/tpami.2021.3097011openalex-snapshot
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond
inferredcites →https://doi.org/10.1109/tsmcb.2011.2168604openalex-snapshot
Extreme Learning Machine for Regression and Multiclass Classification
inferredcites →https://doi.org/10.1111/j.1467-9868.2007.00633.xopenalex-snapshot
Fixed Rank Kriging for Very Large Spatial Data Sets
inferredcites →https://doi.org/10.1111/j.1467-9868.2008.00663.xopenalex-snapshot
Gaussian Predictive Process Models for Large Spatial Data Sets
inferredcites →https://doi.org/10.1111/j.1467-9892.2011.00765.xopenalex-snapshot
Statistics for Spatio‐Temporal Data
inferredcites →https://doi.org/10.1111/j.2517-6161.1988.tb01729.xopenalex-snapshot
Estimation and Model Identification for Continuous Spatial Processes
inferredcites →https://doi.org/10.1137/090771806openalex-snapshot
Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions
inferredcites →https://doi.org/10.1142/9781848162778_0008openalex-snapshot
Liquid State Machines: Motivation, Theory, and Applications
inferredcites →https://doi.org/10.1145/3087556.3087581openalex-snapshot
Randomized Composable Coresets for Matching and Vertex Cover
inferredcites →https://doi.org/10.1145/502512.502546openalex-snapshot
Random projection in dimensionality reduction
inferredcites →https://doi.org/10.1146/annurev-statistics-040120-020733openalex-snapshot
Basis-Function Models in Spatial Statistics
inferredcites →https://doi.org/10.1198/016214506000001437openalex-snapshot
Strictly Proper Scoring Rules, Prediction, and Estimation
inferredcites →https://doi.org/10.1198/016214508000000959openalex-snapshot
Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets
inferredcites →https://doi.org/10.1198/106186006x132178openalex-snapshot
Covariance Tapering for Interpolation of Large Spatial Datasets
inferredcites →https://doi.org/10.1201/9781351769723openalex-snapshot
Spatio-Temporal Statistics with R
inferredcites →https://doi.org/10.1201/9781420072884-12openalex-snapshot
Low-Rank Representations for Spatial Processes
inferredcites →https://doi.org/10.1201/b17115openalex-snapshot
Hierarchical Modeling and Analysis for Spatial Data
inferredcites →https://doi.org/10.1609/aaai.v34i07.7000openalex-snapshot
Random Erasing Data Augmentation
inferredcites →https://doi.org/10.2307/2987782openalex-snapshot
Statistical Analysis of Non-Lattice Data
inferredcites →https://doi.org/10.3390/e21020184openalex-snapshot
Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data
inferredcites →https://doi.org/10.48550/arxiv.1610.04386openalex-snapshot
Random Feature Expansions for Deep Gaussian Processes
inferredcites →https://doi.org/10.48550/arxiv.1611.06740openalex-snapshot
Variational Fourier features for Gaussian processes
inferredcites →https://doi.org/10.48550/arxiv.1806.07572openalex-snapshot
Neural Tangent Kernel: Convergence and Generalization in Neural Networks
inferredcites →https://doi.org/10.7551/mitpress/6444.001.0001openalex-snapshot
State-Space Models with Regime Switching
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s10651-023-00589-0openalex-snapshot
Spatiotemporal modelling of $$\hbox {PM}_{2.5}$$ concentrations in Lombardy (Italy): a comparative study
inferred← citeshttps://doi.org/10.1007/s11222-026-10973-yopenalex-snapshot
Forest-based conformal prediction approach for spatial data
inferred← citeshttps://doi.org/10.1016/j.spasta.2023.100732openalex-snapshot
Using echo state networks to inform physical models for fire front propagation
inferred← citeshttps://doi.org/10.1016/j.spasta.2025.100883openalex-snapshot
Spatial deep convolutional neural networks
inferred← citeshttps://doi.org/10.1016/j.sste.2025.100757openalex-snapshot
BARTSIMP: Flexible spatial covariate modeling and prediction using Bayesian Additive Regression Trees
inferred← citeshttps://doi.org/10.1080/10618600.2026.2670661openalex-snapshot
Low-Rank Neural Regression Models for Spatial Data on Complex Domains
inferred← citeshttps://doi.org/10.1080/26941899.2025.2554883openalex-snapshot
Echo State Networks for Spatio-Temporal Area-Level Data
inferred← citeshttps://doi.org/10.1093/biomtc/ujae057openalex-snapshot
A Bayesian convolutional neural network-based generalized linear model
inferred← citeshttps://doi.org/10.1109/access.2024.3368067openalex-snapshot
ERDeR: The Combination of Statistical Shrinkage Methods and Ensemble Approaches to Improve the Performance of Deep Regression
inferred← citeshttps://doi.org/10.12720/jait.16.7.959-965openalex-snapshot
A Comparative Study of Neural Network Adaptations for Spatial Data
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