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 1581–1590 of 1963 papers

TitleStatusHype
Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression—0
Decoding Mean Field Games from Population and Environment Observations By Gaussian Processes—0
Decoupled Kernel Neural Processes: Neural Network-Parameterized Stochastic Processes using Explicit Data-driven Kernel—0
Decoupled Sparse Gaussian Processes Components]Decoupled Sparse Gaussian Processes Components : Separating Decision Making from Data Manifold Fitting—0
Deep banach space kernels—0
Deep Bayesian Convolutional Networks with Many Channels are Gaussian Processes—0
Deep Bayesian Gaussian Processes for Uncertainty Estimation in Electronic Health Records—0
DeepCoder: Semi-parametric Variational Autoencoders for Automatic Facial Action Coding—0
Deep Compositional Spatial Models—0
Deep Ensemble Kernel Learning—0
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Benchmark Results

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