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 891–900 of 1963 papers

TitleStatusHype
Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing—0
Hypervolume-based Multi-objective Bayesian Optimization with Student-t Processes—0
Gaussian Process Pseudo-Likelihood Models for Sequence Labeling—0
Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies—0
Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder—0
Convolutional Normalizing Flows for Deep Gaussian Processes—0
A Tutorial on Sparse Gaussian Processes and Variational Inference—0
A generalised form for a homogeneous population of structures using an overlapping mixture of Gaussian processes—0
Gaussian Process Optimization with Mutual Information—0
Gaussian Process on the Product of Directional Manifolds—0
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Benchmark Results

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