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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 611–620 of 1963 papers

TitleStatusHype
Adaptive Sampling to Reduce Epistemic Uncertainty Using Prediction Interval-Generation Neural NetworksCode0
Approximate Latent Force Model InferenceCode0
Evaluating the squared-exponential covariance function in Gaussian processes with integral observationsCode0
Evaluating Uncertainty in Deep Gaussian ProcessesCode0
Adversarial Robustness Guarantees for Gaussian ProcessesCode0
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processesCode0
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
Large Linear Multi-output Gaussian Process LearningCode0
Evolving-Graph Gaussian ProcessesCode0
Bayesian Modeling with Gaussian Processes using the GPstuff ToolboxCode0
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Benchmark Results

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