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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 511–520 of 1963 papers

TitleStatusHype
Sparse Gaussian Processes with Spherical Harmonic Features Revisited—0
GP-PCS: One-shot Feature-Preserving Point Cloud Simplification with Gaussian Processes on Riemannian ManifoldsCode0
Stochastic Model Predictive Control Utilizing Bayesian Neural Networks—0
Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black HolesCode1
Clustering based on Mixtures of Sparse Gaussian Processes—0
Chance Constrained Stochastic Optimal Control for Arbitrarily Disturbed LTI Systems Via the One-Sided Vysochanskij-Petunin Inequality—0
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
Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles—0
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Benchmark Results

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