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 791–800 of 1963 papers

TitleStatusHype
Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference—0
Data-Efficient Interactive Multi-Objective Optimization Using ParEGO—0
Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey—0
A Kernel-Based Approach for Modelling Gaussian Processes with Functional Information—0
Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data—0
25 Tweets to Know You: A New Model to Predict Personality with Social Media—0
Data-driven Output Regulation via Gaussian Processes and Luenberger Internal Models—0
Data-Driven Model Selections of Second-Order Particle Dynamics via Integrating Gaussian Processes with Low-Dimensional Interacting Structures—0
Auto-Differentiating Linear Algebra—0
Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes—0
Show:102550
← PrevPage 80 of 197Next →

Benchmark Results

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