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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 781–790 of 1963 papers

TitleStatusHype
Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT—0
An Overview of Uncertainty Quantification Methods for Infinite Neural Networks—0
Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder—0
Convolutional Normalizing Flows for Deep Gaussian 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 Regression constrained by Boundary Value Problems—0
Gaussian Process Regression for Inverse Problems in Linear PDEs—0
Gaussian Process Regression for Maximum Entropy Distribution—0
Efficient Exploration in Continuous-time Model-based Reinforcement Learning—0
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Benchmark Results

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