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 1101–1110 of 1963 papers

TitleStatusHype
Using BART to Perform Pareto Optimization and Quantify its Uncertainties—0
Deep Ensemble Kernel Learning—0
Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning—0
DAG-GPs: Learning Directed Acyclic Graph Structure For Multi-Output Gaussian Processes—0
Activation-level uncertainty in deep neural networks—0
Time Series Counterfactual Inference with Hidden Confounders—0
Optimal Designs of Gaussian Processes with Budgets for Hyperparameter Optimization—0
Fast covariance parameter estimation of spatial Gaussian process models using neural networksCode0
A Tutorial on Sparse Gaussian Processes and Variational Inference—0
Point-Based Value Iteration and Approximately Optimal Dynamic Sensor Selection for Linear-Gaussian Processes—0
Show:102550
← PrevPage 111 of 197Next →

Benchmark Results

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