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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 871–880 of 1963 papers

TitleStatusHype
Variational Nearest Neighbor Gaussian Process—0
Incorporating Sum Constraints into Multitask Gaussian ProcessesCode0
A Kernel-Based Approach for Modelling Gaussian Processes with Functional Information—0
Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder—0
Online Time Series Anomaly Detection with State Space Gaussian Processes—0
A visual exploration of Gaussian Processes and Infinite Neural Networks—0
An Overview of Uncertainty Quantification Methods for Infinite Neural Networks—0
Modeling Human Driver Interactions Using an Infinite Policy Space Through Gaussian Processes—0
Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders—0
Sum-of-Squares Program and Safe Learning On Maximizing the Region of Attraction of Partially Unknown Systems—0
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Benchmark Results

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