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 961–970 of 1963 papers

TitleStatusHype
Dynamic Term Structure Models with Nonlinearities using Gaussian Processes—0
Interpretable deep Gaussian processes with moments—0
Bayesian Parameter Shift Rule in Variational Quantum Eigensolvers—0
Interrelation of equivariant Gaussian processes and convolutional neural networks—0
Inter-state switching in stochastic gene expression: Exact solution, an adiabatic limit and oscillations in molecular distributions—0
Intrinsic Bayesian Optimisation on Complex Constrained Domain—0
A physics-informed Bayesian optimization method for rapid development of electrical machines—0
Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics—0
Bayesian Optimization with Tree-structured Dependencies—0
A note on the smallest eigenvalue of the empirical covariance of causal Gaussian processes—0
Show:102550
← PrevPage 97 of 197Next →

Benchmark Results

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