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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 621–630 of 1963 papers

TitleStatusHype
Learning Constrained Dynamics with Gauss Principle adhering Gaussian ProcessesCode0
Exact Gaussian Processes on a Million Data PointsCode0
Adversarial Robustness Guarantees for Random Deep Neural NetworksCode0
Fast Approximate Multi-output Gaussian ProcessesCode0
Calibrated Computation-Aware Gaussian ProcessesCode0
Learning Integral Representations of Gaussian ProcessesCode0
Bayesian Meta-Learning Through Variational Gaussian ProcessesCode0
Learning ODE Models with Qualitative Structure Using Gaussian ProcessesCode0
Fast covariance parameter estimation of spatial Gaussian process models using neural networksCode0
Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational modelsCode0
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Benchmark Results

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