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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 1521–1530 of 1963 papers

TitleStatusHype
Combining Parametric Land Surface Models with Machine Learning—0
Physics Enhanced Data-Driven Models with Variational Gaussian Processes—0
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data—0
Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction—0
Comparing noisy neural population dynamics using optimal transport distances—0
Complex-Valued Gaussian Processes for Regression—0
Composite Gaussian Processes Flows for Learning Discontinuous Multimodal Policies—0
Composite Gaussian Processes: Scalable Computation and Performance Analysis—0
Composite likelihood estimation of stationary Gaussian processes with a view toward stochastic volatility—0
Compositionally-Warped Gaussian Processes—0
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Benchmark Results

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