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 1591–1600 of 1963 papers

TitleStatusHype
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems—0
Variational Learning on Aggregate Outputs with Gaussian ProcessesCode0
Deep learning generalizes because the parameter-function map is biased towards simple functions—0
Neural Generative Models for Global Optimization with Gradients—0
Heterogeneous Multi-output Gaussian Process PredictionCode0
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processesCode0
Index Set Fourier Series Features for Approximating Multi-dimensional Periodic Kernels—0
Dealing with Categorical and Integer-valued Variables in Bayesian Optimization with Gaussian ProcessesCode0
Bayesian active learning for choice models with deep Gaussian processes—0
Gaussian Process Behaviour in Wide Deep Neural NetworksCode0
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Benchmark Results

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