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 1521–1530 of 1963 papers

TitleStatusHype
Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence—0
A Fast and Greedy Subset-of-Data (SoD) Scheme for Sparsification in Gaussian processes—0
Gaussian Process Accelerated Feldman-Cousins Approach for Physical Parameter Inference—0
Infinite-Horizon Gaussian ProcessesCode0
Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes—0
A Bayesian Perspective of Statistical Machine Learning for Big DataCode0
Targeting Solutions in Bayesian Multi-Objective Optimization: Sequential and Batch Versions—0
Unifying Probabilistic Models for Time-Frequency AnalysisCode0
Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations—0
A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes—0
Show:102550
← PrevPage 153 of 197Next →

Benchmark Results

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