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 411–420 of 1963 papers

TitleStatusHype
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian ManifoldsCode0
A spectrum of physics-informed Gaussian processes for regression in engineering—0
A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes—0
Convolutional Deep Kernel MachinesCode0
Data-driven Modeling and Inference for Bayesian Gaussian Process ODEs via Double Normalizing FlowsCode0
Modelling Irrational Behaviour of Residential End Users using Non-Stationary Gaussian Processes—0
Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple KernelCode0
On Distributed and Asynchronous Sampling of Gaussian Processes for Sequential Binary Hypothesis Testing—0
Scalable Model-Based Gaussian Process Clustering—0
Promises of Deep Kernel Learning for Control Synthesis—0
Show:102550
← PrevPage 42 of 197Next →

Benchmark Results

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