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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 1061–1070 of 1963 papers

TitleStatusHype
Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models - a Gentle Tutorial—0
Variational Mixture of Gaussian Process Experts—0
Variational Nearest Neighbor Gaussian Process—0
VBALD - Variational Bayesian Approximation of Log Determinants—0
Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks—0
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels—0
Vehicle Dynamics Modeling for Autonomous Racing Using Gaussian Processes—0
Bayesian Circular Regression with von Mises Quasi-Processes—0
Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes—0
Warped Gaussian Processes in Remote Sensing Parameter Estimation and Causal Inference—0
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Benchmark Results

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