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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 561–570 of 1963 papers

TitleStatusHype
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
Bayesian Semi-supervised Learning with Graph Gaussian ProcessesCode0
Model-based Reinforcement Learning for Continuous Control with Posterior SamplingCode0
Gaussian Process Random FieldsCode0
Bayesian Structured Prediction Using Gaussian ProcessesCode0
Few-Shot Speech Deepfake Detection Adaptation with Gaussian ProcessesCode0
Flexible and efficient emulation of spatial extremes processes via variational autoencodersCode0
Efficient Inference in Multi-task Cox Process ModelsCode0
How Good are Low-Rank Approximations in Gaussian Process Regression?Code0
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable ModelsCode0
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Benchmark Results

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