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 501–510 of 1963 papers

TitleStatusHype
Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies—0
Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial LibrariesCode0
Neural variational Data Assimilation with Uncertainty Quantification using SPDE priors—0
Bayesian Causal Inference with Gaussian Process NetworksCode0
Quantum-Assisted Hilbert-Space Gaussian Process RegressionCode0
Semi-parametric Expert Bayesian Network Learning with Gaussian Processes and Horseshoe Priors—0
A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional DataCode0
Towards Improved Variational Inference for Deep Bayesian Models—0
Sparse discovery of differential equations based on multi-fidelity Gaussian process—0
Simulation Based Bayesian OptimizationCode0
Show:102550
← PrevPage 51 of 197Next →

Benchmark Results

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