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 521–530 of 1963 papers

TitleStatusHype
Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles—0
A switching Gaussian process latent force model for the identification of mechanical systems with a discontinuous nonlinearityCode0
Traffic State Estimation from Vehicle Trajectories with Anisotropic Gaussian ProcessesCode1
Calibrating Transformers via Sparse Gaussian ProcessesCode1
Neural-BO: A Black-box Optimization Algorithm using Deep Neural NetworksCode1
Learning-based Position and Stiffness Feedforward Control of Antagonistic Soft Pneumatic Actuators using Gaussian Processes—0
Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian ProcessesCode0
Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces—0
Bayesian Kernelized Tensor Factorization as Surrogate for Bayesian Optimization—0
Random forests for binary geospatial data—0
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Benchmark Results

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