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 461–470 of 1963 papers

TitleStatusHype
Functional Causal Bayesian Optimization—0
Monte Carlo inference for semiparametric Bayesian regression—0
Representing and Learning Functions Invariant Under Crystallographic Groups—0
Training-Free Neural Active Learning with Initialization-Robustness GuaranteesCode0
Vehicle Dynamics Modeling for Autonomous Racing Using Gaussian Processes—0
Graph Classification Gaussian Processes via Spectral Features—0
Memory-Based Dual Gaussian Processes for Sequential LearningCode1
Global universal approximation of functional input maps on weighted spacesCode0
Constrained Causal Bayesian OptimizationCode1
Taylorformer: Probabilistic Modelling for Random Processes including Time SeriesCode0
Show:102550
← PrevPage 47 of 197Next →

Benchmark Results

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