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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 641–650 of 1963 papers

TitleStatusHype
Challenges in Gaussian Processes for Non Intrusive Load MonitoringCode0
MAGMA: Inference and Prediction with Multi-Task Gaussian ProcessesCode0
Adaptive RKHS Fourier Features for Compositional Gaussian Process ModelsCode0
Fast covariance parameter estimation of spatial Gaussian process models using neural networksCode0
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian ProcessesCode0
Bayesian Learning-Based Adaptive Control for Safety Critical SystemsCode0
Fast Approximate Multi-output Gaussian ProcessesCode0
How Good are Low-Rank Approximations in Gaussian Process Regression?Code0
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
Fully Bayesian inference for latent variable Gaussian process modelsCode0
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Benchmark Results

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