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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 1281–1290 of 1963 papers

TitleStatusHype
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural NetworksCode0
Conditional Deep Gaussian Processes: multi-fidelity kernel learningCode0
Linearly Constrained Neural NetworksCode0
Linearly Constrained Gaussian Processes with Boundary Conditions—0
A Machine Consciousness architecture based on Deep Learning and Gaussian Processes—0
Estimation of Z-Thickness and XY-Anisotropy of Electron Microscopy Images using Gaussian ProcessesCode0
Transport Gaussian Processes for Regression—0
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness—0
Multi-class Gaussian Process Classification with Noisy InputsCode1
Estimating Latent Demand of Shared Mobility through Censored Gaussian ProcessesCode0
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Benchmark Results

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