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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 1931–1940 of 1963 papers

TitleStatusHype
Locally adaptive factor processes for multivariate time series—0
Information fusion in multi-task Gaussian processes—0
Variable noise and dimensionality reduction for sparse Gaussian processes—0
Bayesian Modeling with Gaussian Processes using the GPstuff ToolboxCode0
Robust Filtering and Smoothing with Gaussian Processes—0
Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes—0
Additive Gaussian ProcessesCode0
Active learning of neural response functions with Gaussian processes—0
Analytical Results for the Error in Filtering of Gaussian Processes—0
Universal low-rank matrix recovery from Pauli measurements—0
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Benchmark Results

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