SOTAVerified

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 1531–1540 of 1963 papers

TitleStatusHype
Computational Graph Completion—0
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models—0
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference—0
Conditional Generative Modeling for Images, 3D Animations, and Video—0
Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation—0
Conditional Neural Processes for Molecules—0
Conditioning of Banach Space Valued Gaussian Random Variables: An Approximation Approach Based on Martingales—0
Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes—0
Conformal Prediction for Manifold-based Source Localization with Gaussian Processes—0
Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes—0
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Benchmark Results

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