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 421–430 of 1963 papers

TitleStatusHype
A brief note on understanding neural networks as Gaussian processes—0
Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference—0
A Learning-based Nonlinear Model Predictive Controller for a Real Go-Kart based on Black-box Dynamics Modeling through Gaussian Processes—0
DEBOSH: Deep Bayesian Shape Optimization—0
Physics Enhanced Data-Driven Models with Variational Gaussian Processes—0
Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression—0
Combining Parametric Land Surface Models with Machine Learning—0
Decoding Mean Field Games from Population and Environment Observations By Gaussian Processes—0
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation—0
Quantum neural networks form Gaussian processes—0
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Benchmark Results

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