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 371–380 of 1963 papers

TitleStatusHype
Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor DataCode0
Solving High Frequency and Multi-Scale PDEs with Gaussian ProcessesCode1
Kernel-, mean- and noise-marginalised Gaussian processes for exoplanet transits and H_0 inferenceCode0
Neural SPDE solver for uncertainty quantification in high-dimensional space-time dynamics—0
SemiGPC: Distribution-Aware Label Refinement for Imbalanced Semi-Supervised Learning Using Gaussian Processes—0
Gaussian Processes on Cellular Complexes—0
Data-Driven Model Selections of Second-Order Particle Dynamics via Integrating Gaussian Processes with Low-Dimensional Interacting Structures—0
Robust and Conjugate Gaussian Process RegressionCode0
Variational Gaussian Processes For Linear Inverse Problems—0
Stochastic Gradient Descent for Gaussian Processes Done RightCode1
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Benchmark Results

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