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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 1161–1170 of 1963 papers

TitleStatusHype
Orthogonally Decoupled Variational Fourier Features—0
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian ProcessesCode1
Bayesian Deep Ensembles via the Neural Tangent KernelCode1
Characteristics of Monte Carlo Dropout in Wide Neural Networks—0
srMO-BO-3GP: A sequential regularized multi-objective constrained Bayesian optimization for design applications—0
Doubly infinite residual neural networks: a diffusion process approach—0
A Perspective on Gaussian Processes for Earth Observation—0
Motor cortex mapping using active gaussian processes—0
Overview of Gaussian process based multi-fidelity techniques with variable relationship between fidelities—0
Sparse Gaussian Processes with Spherical Harmonic Features—0
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Benchmark Results

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