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 1301–1310 of 1963 papers

TitleStatusHype
A brief note on understanding neural networks as Gaussian processes—0
A Bulirsch-Stoer algorithm using Gaussian processes—0
Accelerating ABC methods using Gaussian processes—0
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes—0
Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty—0
Accurate and Uncertainty-Aware Multi-Task Prediction of HEA Properties Using Prior-Guided Deep Gaussian Processes—0
ASMCNN: An Efficient Brain Extraction Using Active Shape Model and Convolutional Neural Networks—0
A chain rule for the expected suprema of Gaussian processes—0
A Chain Rule for the Expected Suprema of Bernoulli Processes—0
A comparison of apartment rent price prediction using a large dataset: Kriging versus DNN—0
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Benchmark Results

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