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 41–50 of 1963 papers

TitleStatusHype
Data-driven Approach for Interpolation of Sparse DataCode0
Power Flow Approximations for Multiphase Distribution Networks using Gaussian Processes—0
Low-rank computation of the posterior mean in Multi-Output Gaussian Processes—0
TopSpace: spatial topic modeling for unsupervised discovery of multicellular spatial tissue structures in multiplex imaging—0
A Taylor Series Approach to Correction of Input Errors in Gaussian Process Regression—0
Evaluating Uncertainty in Deep Gaussian ProcessesCode0
Gaussian behaviors: representations and data-driven control—0
Uncertainty-Aware Trajectory Prediction via Rule-Regularized Heteroscedastic Deep ClassificationCode0
Towards Scalable Bayesian Optimization via Gradient-Informed Bayesian Neural Networks—0
Learning-based decentralized control with collision avoidance for multi-agent systems—0
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Benchmark Results

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