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 1211–1220 of 1963 papers

TitleStatusHype
Sparse Gaussian Processes with Spherical Harmonic Features—0
Overview of Gaussian process based multi-fidelity techniques with variable relationship between fidelities—0
Multi-fidelity modeling with different input domain definitions using Deep Gaussian Processes—0
Is SGD a Bayesian sampler? Well, almost—0
Intrinsic Gaussian Processes on Manifolds and Their Accelerations by Symmetry—0
Green Machine Learning via Augmented Gaussian Processes and Multi-Information Source Optimization—0
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation—0
Beyond Grids: Multi-objective Bayesian Optimization With Adaptive DiscretizationCode0
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian OptimizationCode0
Infinite attention: NNGP and NTK for deep attention networks—0
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Benchmark Results

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