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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 161–170 of 1963 papers

TitleStatusHype
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian ProcessesCode1
Bayesian Deep Ensembles via the Neural Tangent KernelCode1
Data-Driven Discovery of Molecular Photoswitches with Multioutput Gaussian ProcessesCode1
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian ProcessesCode1
Matérn Gaussian processes on Riemannian manifoldsCode1
70 years of machine learning in geoscience in reviewCode1
Syn2Real Transfer Learning for Image Deraining using Gaussian ProcessesCode1
Variational Auto-Regressive Gaussian Processes for Continual LearningCode1
Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving CarsCode1
Quadruply Stochastic Gaussian ProcessesCode1
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Benchmark Results

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