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 611–620 of 1963 papers

TitleStatusHype
Branching Gaussian Processes with Applications to Spatiotemporal Reconstruction of 3D Trees—0
Evaluating Hospital Case Cost Prediction Models Using Azure Machine Learning Studio—0
BrowNNe: Brownian Nonlocal Neurons & Activation Functions—0
Forecasting of commercial sales with large scale Gaussian Processes—0
Evaluation of Deep Gaussian Processes for Text Classification—0
Evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems—0
Application of machine learning to gas flaring—0
Evolution of Covariance Functions for Gaussian Process Regression using Genetic Programming—0
Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty—0
Faster variational inducing input Gaussian process classification—0
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Benchmark Results

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