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 1651–1660 of 1963 papers

TitleStatusHype
Distribution Calibration for Regression—0
Double-descent curves in neural networks: a new perspective using Gaussian processes—0
Doubly infinite residual neural networks: a diffusion process approach—0
Doubly Sparse Variational Gaussian Processes—0
Dream to Explore: Adaptive Simulations for Autonomous Systems—0
Dynamic Term Structure Models with Nonlinearities using Gaussian Processes—0
Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processes—0
Efficient acquisition rules for model-based approximate Bayesian computation—0
Efficient Approximate Inference with Walsh-Hadamard Variational Inference—0
Efficient Bayesian Inference for a Gaussian Process Density Model—0
Show:102550
← PrevPage 166 of 197Next →

Benchmark Results

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