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 661–670 of 1963 papers

TitleStatusHype
Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary SystemsCode0
Gaussian Process on the Product of Directional Manifolds—0
Reconstructing the Hubble parameter with future Gravitational Wave missions using Machine Learning—0
Safe Machine-Learning-supported Model Predictive Force and Motion Control in Robotics—0
Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles—0
A switching Gaussian process latent force model for the identification of mechanical systems with a discontinuous nonlinearityCode0
Learning-based Position and Stiffness Feedforward Control of Antagonistic Soft Pneumatic Actuators using Gaussian Processes—0
Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian ProcessesCode0
Bayesian Kernelized Tensor Factorization as Surrogate for Bayesian Optimization—0
Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces—0
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Benchmark Results

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