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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 71–80 of 1963 papers

TitleStatusHype
Constrained Causal Bayesian OptimizationCode1
Convolutional conditional neural processes for local climate downscalingCode1
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical SimulationsCode1
Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical ReactorsCode1
Active Bayesian Causal InferenceCode1
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black HolesCode1
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decompositionCode1
A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics IdentificationCode1
Building 3D Morphable Models from a Single ScanCode1
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Benchmark Results

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