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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 1561–1570 of 1963 papers

TitleStatusHype
Learning Invariances using the Marginal Likelihood—0
Deep Convolutional Networks as shallow Gaussian ProcessesCode0
Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing—0
Multi-Output Convolution Spectral Mixture for Gaussian Processes—0
Multitask Gaussian Process with Hierarchical Latent Interactions—0
Assessing Quality Estimation Models for Sentence-Level Prediction—0
Compressible Spectral Mixture Kernels with Sparse Dependency Structures for Gaussian Processes—0
Remote sensing image regression for heterogeneous change detection—0
Global optimization using Gaussian Processes to estimate biological parameters from image data—0
Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces—0
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Benchmark Results

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