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 71–80 of 1963 papers

TitleStatusHype
Example-guided learning of stochastic human driving policies using deep reinforcement learningCode1
Learning safety in model-based Reinforcement Learning using MPC and Gaussian ProcessesCode1
Fast and robust Bayesian Inference using Gaussian Processes with GPryCode1
Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical ReactorsCode1
Understanding of the properties of neural network approaches for transient light curve approximationsCode1
The Neural Process Family: Survey, Applications and PerspectivesCode1
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact caseCode1
Light curve completion and forecasting using fast and scalable Gaussian processes (MuyGPs)Code1
Low-Precision Arithmetic for Fast Gaussian ProcessesCode1
Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian ProcessesCode1
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Benchmark Results

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