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 1041–1050 of 1963 papers

TitleStatusHype
A Tutorial on Sparse Gaussian Processes and Variational Inference—0
Point-Based Value Iteration and Approximately Optimal Dynamic Sensor Selection for Linear-Gaussian Processes—0
Learning Structures in Earth Observation Data with Gaussian Processes—0
Gaussian Process Regression constrained by Boundary Value Problems—0
Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior—0
Parameter Identification for Digital Fabrication: A Gaussian Process Learning Approach—0
Active Learning for Deep Gaussian Process SurrogatesCode0
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian ProcessesCode1
Structured learning of rigid-body dynamics: A survey and unified view from a robotics perspective—0
Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes—0
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Benchmark Results

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