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
Finite size corrections for neural network Gaussian processes—0
Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting—0
Forecasting intermittent time series with Gaussian Processes and Tweedie likelihood—0
Forecasting of commercial sales with large scale Gaussian Processes—0
Forecasting Wireless Demand with Extreme Values using Feature Embedding in Gaussian Processes—0
Fractional Barndorff-Nielsen and Shephard model: applications in variance and volatility swaps, and hedging—0
Frequency Domain Gaussian Process Models for H^ Uncertainties—0
Frequency-domain Gaussian Process Models for H_ Uncertainties—0
From Prediction to Action: Critical Role of Performance Estimation for Machine-Learning-Driven Materials Discovery—0
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes—0
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Benchmark Results

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