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 1241–1250 of 1963 papers

TitleStatusHype
How Good are Low-Rank Approximations in Gaussian Process Regression?Code0
Predicting the outputs of finite deep neural networks trained with noisy gradients—0
Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data—0
Variational Inference with Vine Copulas: An efficient Approach for Bayesian Computer Model CalibrationCode0
Gaussian-Dirichlet Random Fields for Inference over High Dimensional Categorical Observations—0
Baryons from Mesons: A Machine Learning Perspective—0
Deep Bayesian Gaussian Processes for Uncertainty Estimation in Electronic Health Records—0
Scaling up Kernel Ridge Regression via Locality Sensitive Hashing—0
Deep Reinforcement Learning with Weighted Q-Learning—0
aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture—0
Show:102550
← PrevPage 125 of 197Next →

Benchmark Results

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