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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 1081–1090 of 1963 papers

TitleStatusHype
Sequence Alignment with Dirichlet Process Mixtures—0
Sequential Estimation of Gaussian Process-based Deep State-Space Models—0
Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization—0
Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes—0
Sharp Calibrated Gaussian Processes—0
SHEF-Lite 2.0: Sparse Multi-task Gaussian Processes for Translation Quality Estimation—0
Ensembling methods for countrywide short term forecasting of gas demand—0
Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes—0
Short-term Volatility Estimation for High Frequency Trades using Gaussian processes (GPs)—0
SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data—0
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Benchmark Results

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