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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 871–880 of 1963 papers

TitleStatusHype
DADEE: Well-calibrated uncertainty quantification in neural networks for barriers-based robot safety—0
Gaussian Process Volatility Model—0
Current Methods for Drug Property Prediction in the Real World—0
Gaussian process surrogate model to approximate power grid simulators -- An application to the certification of a congestion management controller—0
Gaussian Process Surrogate Models for Neural Networks—0
Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data—0
Correlational Gaussian Processes for Cross-Domain Visual Recognition—0
AUGUR, A flexible and efficient optimization algorithm for identification of optimal adsorption sites—0
A Generalized Unified Skew-Normal Process with Neural Bayes Inference—0
Gaussian Process Regression for Maximum Entropy Distribution—0
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Benchmark Results

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