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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 1131–1140 of 1963 papers

TitleStatusHype
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural ProcessesCode0
Gaussian Processes for Traffic Speed Prediction at Different Aggregation Levels—0
Gaussian Process Latent Variable Flows for Massively Missing Data—0
Neural Networks as Inter-Domain Inducing Points—0
Neural Linear Models with Functional Gaussian Process Priors—0
Functional Priors for Bayesian Neural Networks through Wasserstein Distance Minimization to Gaussian Processes—0
Decoupled Sparse Gaussian Processes Components]Decoupled Sparse Gaussian Processes Components : Separating Decision Making from Data Manifold Fitting—0
The Gaussian Process Latent Autoregressive Model—0
Model-based Reinforcement Learning for Continuous Control with Posterior SamplingCode0
The Impact of Data on the Stability of Learning-Based Control- Extended Version—0
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Benchmark Results

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