SOTAVerified

Gaussian Processes

Gaussian Processes is a powerful framework for several machine learning tasks such as regression, classification and inference. Given a finite set of input output training data that is generated out of a fixed (but possibly unknown) function, the framework models the unknown function as a stochastic process such that the training outputs are a finite number of jointly Gaussian random variables, whose properties can then be used to infer the statistics (the mean and variance) of the function at test values of input.

Source: Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization

Papers

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Showing 1101–1125 of 1963 papers

TitleStatusHype
Using BART to Perform Pareto Optimization and Quantify its Uncertainties—0
Deep Ensemble Kernel Learning—0
Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning—0
DAG-GPs: Learning Directed Acyclic Graph Structure For Multi-Output Gaussian Processes—0
Activation-level uncertainty in deep neural networks—0
Time Series Counterfactual Inference with Hidden Confounders—0
Optimal Designs of Gaussian Processes with Budgets for Hyperparameter Optimization—0
Fast covariance parameter estimation of spatial Gaussian process models using neural networksCode0
A Tutorial on Sparse Gaussian Processes and Variational Inference—0
Point-Based Value Iteration and Approximately Optimal Dynamic Sensor Selection for Linear-Gaussian Processes—0
Gaussian Process Regression constrained by Boundary Value Problems—0
Learning Structures in Earth Observation Data with Gaussian Processes—0
Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior—0
Parameter Identification for Digital Fabrication: A Gaussian Process Learning Approach—0
Active Learning for Deep Gaussian Process SurrogatesCode0
Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes—0
Structured learning of rigid-body dynamics: A survey and unified view from a robotics perspective—0
Warped Gaussian Processes in Remote Sensing Parameter Estimation and Causal Inference—0
Mapping Leaf Area Index with a Smartphone and Gaussian Processes—0
Spectral band selection for vegetation properties retrieval using Gaussian processes regression—0
Deep Gaussian Processes for geophysical parameter retrieval—0
Nonlinear Distribution Regression for Remote Sensing Applications—0
Physics-Aware Gaussian Processes in Remote Sensing—0
Understanding Climate Impacts on Vegetation with Gaussian Processes in Granger Causality—0
Fusing Optical and SAR time series for LAI gap filling with multioutput Gaussian processes—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ICKy, periodicRoot mean square error (RMSE)0.03—Unverified