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 371–380 of 1963 papers

TitleStatusHype
Estimation of Dynamic Gaussian ProcessesCode0
Challenges in Gaussian Processes for Non Intrusive Load MonitoringCode0
Epistemic Uncertainty in Conformal Scores: A Unified ApproachCode0
Integrated Gradient attribution for Gaussian Processes with non-Gaussian likelihoodsCode0
Estimation of Z-Thickness and XY-Anisotropy of Electron Microscopy Images using Gaussian ProcessesCode0
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
Continuous Optimization Benchmarks by SimulationCode0
Chained Gaussian ProcessesCode0
Heterogeneous Multi-Task Gaussian Cox ProcessesCode0
Efficient Modeling of Latent Information in Supervised Learning using Gaussian ProcessesCode0
Show:102550
← PrevPage 38 of 197Next →

Benchmark Results

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