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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 1121–1130 of 1963 papers

TitleStatusHype
Gaussian Process Molecule Property Prediction with FlowMO—0
Predicting the Outputs of Finite Networks Trained with Noisy Gradients—0
Efficient Exploration for Model-based Reinforcement Learning with Continuous States and Actions—0
Multi-task Causal Learning with Gaussian ProcessesCode1
Semi-Supervised Image Deraining using Gaussian ProcessesCode1
Stein Variational Gaussian ProcessesCode0
An Intuitive Tutorial to Gaussian Process RegressionCode1
A Joint introduction to Gaussian Processes and Relevance Vector Machines with Connections to Kalman filtering and other Kernel Smoothers—0
Time series forecasting with Gaussian Processes needs priorsCode1
Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels—0
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Benchmark Results

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