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 1321–1330 of 1963 papers

TitleStatusHype
Randomly Projected Additive Gaussian Processes for RegressionCode0
Disentangling Trainability and Generalization in Deep Neural Networks—0
Scalable Gaussian Process Regression for Kernels with a Non-Stationary Phase—0
Quantile Propagation for Wasserstein-Approximate Gaussian ProcessesCode0
Teaching robots to perceive time -- A reinforcement learning approach (Extended version)—0
Active emulation of computer codes with Gaussian processes -- Application to remote sensing—0
On the relationship between multitask neural networks and multitask Gaussian Processes—0
Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax—0
lgpr: An interpretable nonparametric method for inferring covariate effects from longitudinal dataCode0
Warped Input Gaussian Processes for Time Series ForecastingCode0
Show:102550
← PrevPage 133 of 197Next →

Benchmark Results

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