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 781–790 of 1963 papers

TitleStatusHype
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models—0
Decoupled Kernel Neural Processes: Neural Network-Parameterized Stochastic Processes using Explicit Data-driven Kernel—0
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation—0
Decoding Mean Field Games from Population and Environment Observations By Gaussian Processes—0
Algorithmic Linearly Constrained Gaussian Processes—0
Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression—0
DEBOSH: Deep Bayesian Shape Optimization—0
A Learning-based Nonlinear Model Predictive Controller for a Real Go-Kart based on Black-box Dynamics Modeling through Gaussian Processes—0
Data Fusion with Latent Map Gaussian Processes—0
Data Efficient Prediction of excited-state properties using Quantum Neural Networks—0
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Benchmark Results

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