SOTAVerified

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 841–850 of 1963 papers

TitleStatusHype
Graph Classification Gaussian Processes via Spectral Features—0
Graph Classification Gaussian Processes via Hodgelet Spectral Features—0
An Overview of Uncertainty Quantification Methods for Infinite Neural Networks—0
Graph Convolutional Gaussian Processes For Link Prediction—0
Genus expansion for non-linear random matrix ensembles with applications to neural networks—0
Graphical LASSO Based Model Selection for Time Series—0
Efficient Exploration in Continuous-time Model-based Reinforcement Learning—0
Bayesian Sparse Factor Analysis with Kernelized Observations—0
Fast Risk Assessment in Power Grids through Novel Gaussian Process and Active Learning—0
High-dimensional near-optimal experiment design for drug discovery via Bayesian sparse sampling—0
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Benchmark Results

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