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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 901–910 of 1963 papers

TitleStatusHype
Structure-Aware Random Fourier Kernel for Graphs—0
Learning to Learn Dense Gaussian Processes for Few-Shot Learning—0
A Novel Gaussian Process Based Ground Segmentation Algorithm with Local-Smoothness Estimation—0
Dependence between Bayesian neural network units—0
The Fixed-b Limiting Distribution and the ERP of HAR Tests Under Nonstationarity—0
Contextual Combinatorial Multi-output GP Bandits with Group Constraints—0
Improved Inverse-Free Variational Bounds for Sparse Gaussian Processes—0
Transfer Learning with Gaussian Processes for Bayesian OptimizationCode0
Accounting for Gaussian Process Imprecision in Bayesian OptimizationCode0
Non-separable Spatio-temporal Graph Kernels via SPDEs—0
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Benchmark Results

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