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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 781–790 of 1963 papers

TitleStatusHype
Gaussian Process Surrogate Models for Neural Networks—0
Bayesian Optimization with Informative Covariance—0
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs—0
Data-Driven Stochastic AC-OPF using Gaussian ProcessesCode0
Correcting Model Bias with Sparse Implicit Processes—0
Kullback-Leibler and Renyi divergences in reproducing kernel Hilbert space and Gaussian process settings—0
Learning inducing points and uncertainty on molecular data by scalable variational Gaussian processes—0
Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction—0
Infinite-Fidelity Coregionalization for Physical Simulation—0
Off-the-grid learning of mixtures from a continuous dictionary—0
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Benchmark Results

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