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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 301–310 of 1963 papers

TitleStatusHype
Sparse Variational Contaminated Noise Gaussian Process Regression with Applications in Geomagnetic Perturbations Forecasting—0
Re-Envisioning Numerical Information Field Theory (NIFTy.re): A Library for Gaussian Processes and Variational Inference—0
Gradient-enhanced deep Gaussian processes for multifidelity modelling—0
Enhancing Mean-Reverting Time Series Prediction with Gaussian Processes: Functional and Augmented Data Structures in Financial Forecasting—0
Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive OrdersCode0
Global Safe Sequential Learning via Efficient Knowledge TransferCode0
Motion Code: Robust Time Series Classification and Forecasting via Sparse Variational Multi-Stochastic Processes LearningCode0
Data-Driven Stochastic AC-OPF using Gaussian ProcessesCode0
Resilience of Rademacher chaos of low degree—0
Nowcasting with Mixed Frequency Data Using Gaussian Processes—0
Show:102550
← PrevPage 31 of 197Next →

Benchmark Results

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