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 891–900 of 1963 papers

TitleStatusHype
Hyperspectral recovery from RGB images using Gaussian Processes—0
Hypervolume-based Multi-objective Bayesian Optimization with Student-t Processes—0
Bayesian Relational Generative Model for Scalable Multi-modal Learning—0
Identifying Causal Direction via Variational Bayesian Compression—0
A dependent partition-valued process for multitask clustering and time evolving network modelling—0
Inferring power system dynamics from synchrophasor data using Gaussian processes—0
Infinite-Fidelity Coregionalization for Physical Simulation—0
Information Flow Rate for Cross-Correlated Stochastic Processes—0
Efficient Bayesian Inference for a Gaussian Process Density Model—0
Efficient Approximate Inference with Walsh-Hadamard Variational Inference—0
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Benchmark Results

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