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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 1776–1800 of 1963 papers

TitleStatusHype
Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis—0
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences—0
Gaussian Processes and Reproducing Kernels: Connections and Equivalences—0
Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces—0
Gaussian processes based data augmentation and expected signature for time series classification—0
Gaussian Processes for Analyzing Positioned Trajectories in Sports—0
Gaussian processes for Bayesian inverse problems associated with linear partial differential equations—0
Gaussian processes for dynamics learning in model predictive control—0
Gaussian Processes for Music Audio Modelling and Content Analysis—0
Gaussian Processes for Natural Language Processing—0
Gaussian Processes for Nonlinear Signal Processing—0
Gaussian Processes for Survival Analysis—0
Gaussian Processes for Traffic Speed Prediction at Different Aggregation Levels—0
Gaussian Processes indexed on the symmetric group: prediction and learning—0
Gaussian Processes in Power Systems: Techniques, Applications, and Future Works—0
Gaussian Processes on Cellular Complexes—0
Gaussian Processes on Distributions based on Regularized Optimal Transport—0
Gaussian Processes on Graphs via Spectral Kernel Learning—0
Gaussian Processes on Hypergraphs—0
Gaussian Processes Over Graphs—0
Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect—0
Gaussian Processes with Context-Supported Priors for Active Object Localization—0
Gaussian Processes with Differential Privacy—0
Gaussian Processes with Noisy Regression Inputs for Dynamical Systems—0
Gaussian Processes with State-Dependent Noise for Stochastic Control—0
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Benchmark Results

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