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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 1071–1080 of 1963 papers

TitleStatusHype
Decoupled Sparse Gaussian Processes Components]Decoupled Sparse Gaussian Processes Components : Separating Decision Making from Data Manifold Fitting—0
Gaussian Process Latent Variable Flows for Massively Missing Data—0
Neural Networks as Inter-Domain Inducing Points—0
The Gaussian Process Latent Autoregressive Model—0
Functional Priors for Bayesian Neural Networks through Wasserstein Distance Minimization to Gaussian Processes—0
Neural Linear Models with Functional Gaussian Process Priors—0
Model-based Reinforcement Learning for Continuous Control with Posterior SamplingCode0
Design of Experiments for Verifying Biomolecular Networks—0
The Impact of Data on the Stability of Learning-Based Control- Extended Version—0
Safe model-based design of experiments using Gaussian processes—0
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Benchmark Results

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