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 1631–1640 of 1963 papers

TitleStatusHype
Dimensionality Reduction as Probabilistic Inference—0
Dimensionality Reduction Techniques for Global Bayesian Optimisation—0
Direct Integration of Recursive Gaussian Process Regression Into Extended Kalman Filters With Application to Vapor Compression Cycle Control—0
Dirichlet Logistic Gaussian Processes for Evaluation of Black-Box Stochastic Systems under Complex Requirements—0
Discovering and forecasting extreme events via active learning in neural operators—0
Discovery of Probabilistic Dirichlet-to-Neumann Maps on Graphs—0
Discriminative training for Convolved Multiple-Output Gaussian processes—0
Disentangling the Gauss-Newton Method and Approximate Inference for Neural Networks—0
Disentangling Trainability and Generalization in Deep Learning—0
Disentangling Trainability and Generalization in Deep Neural Networks—0
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Benchmark Results

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