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 1741–1750 of 1963 papers

TitleStatusHype
Signal-based Bayesian Seismic Monitoring—0
Linearly constrained Gaussian processes—0
Embarrassingly Parallel Inference for Gaussian ProcessesCode0
Approximate Bayes learning of stochastic differential equations—0
Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An application to high-throughput toxicity testing—0
Optimal Detection of Faulty Traffic Sensors Used in Route Planning—0
Query Efficient Posterior Estimation in Scientific Experiments via Bayesian Active Learning—0
A Gaussian Process Regression Model for Distribution Inputs—0
Multi-view Regularized Gaussian Processes—0
Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection—0
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Benchmark Results

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