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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 1601–1610 of 1963 papers

TitleStatusHype
Deep Gaussian Processes with Decoupled Inducing Inputs—0
Deep Horseshoe Gaussian Processes—0
Deep Importance Sampling based on Regression for Model Inversion and Emulation—0
Deep kernel processes—0
Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics—0
Deep learning generalizes because the parameter-function map is biased towards simple functions—0
Deep Manifold Prior—0
Meta-Learning Mean Functions for Gaussian Processes—0
Deep Neural Networks as Point Estimates for Deep Gaussian Processes—0
Quantum neural networks form Gaussian processes—0
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Benchmark Results

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