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 401–425 of 1963 papers

TitleStatusHype
Damage detection in operational wind turbine blades using a new approach based on machine learning—0
Data Association with Gaussian Processes—0
Aggregating Dependent Gaussian Experts in Local Approximation—0
Data-Driven Approaches for Modelling Target Behaviour—0
A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes—0
A Robust Asymmetric Kernel Function for Bayesian Optimization, with Application to Image Defect Detection in Manufacturing Systems—0
A Fully-Automated Framework Integrating Gaussian Process Regression and Bayesian Optimization to Design Pin-Fins—0
Data-driven Bayesian Control of Port-Hamiltonian Systems—0
Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction—0
Data-driven Force Observer for Human-Robot Interaction with Series Elastic Actuators using Gaussian Processes—0
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks—0
Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes—0
Data-Driven Model Selections of Second-Order Particle Dynamics via Integrating Gaussian Processes with Low-Dimensional Interacting Structures—0
Data-driven Output Regulation via Gaussian Processes and Luenberger Internal Models—0
Quantum neural networks form Gaussian processes—0
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data—0
A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes—0
Data-Efficient Interactive Multi-Objective Optimization Using ParEGO—0
A brief note on understanding neural networks as Gaussian processes—0
A Kernel-Based Approach for Modelling Gaussian Processes with Functional Information—0
Data Fusion with Latent Map Gaussian Processes—0
Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference—0
Physics Enhanced Data-Driven Models with Variational Gaussian Processes—0
DEBOSH: Deep Bayesian Shape Optimization—0
Combining Parametric Land Surface Models with Machine Learning—0
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Benchmark Results

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