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

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Showing 1571–1580 of 1963 papers

TitleStatusHype
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
Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey—0
Data-Efficient Interactive Multi-Objective Optimization Using ParEGO—0
Data Efficient Prediction of excited-state properties using Quantum Neural Networks—0
Data Fusion with Latent Map Gaussian Processes—0
DEBOSH: Deep Bayesian Shape Optimization—0
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Benchmark Results

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