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 1061–1070 of 1963 papers

TitleStatusHype
Stochastic Deep Gaussian Processes over GraphsCode1
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data—0
Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes—0
Variance based sensitivity analysis for Monte Carlo and importance sampling reliability assessment with Gaussian processes—0
Equivalence of Convergence Rates of Posterior Distributions and Bayes Estimators for Functions and Nonparametric Functionals—0
Exploration in Online Advertising Systems with Deep Uncertainty-Aware LearningCode1
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural ProcessesCode0
All You Need is a Good Functional Prior for Bayesian Deep Learning—0
Gaussian Processes for Traffic Speed Prediction at Different Aggregation Levels—0
Building 3D Morphable Models from a Single ScanCode1
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Benchmark Results

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