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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 431–440 of 1963 papers

TitleStatusHype
A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes—0
A brief note on understanding neural networks as Gaussian processes—0
Deep Bayesian Convolutional Networks with Many Channels are Gaussian Processes—0
Deep Bayesian Gaussian Processes for Uncertainty Estimation in Electronic Health Records—0
Physics Enhanced Data-Driven Models with Variational Gaussian Processes—0
DeepCoder: Semi-parametric Variational Autoencoders for Automatic Facial Action Coding—0
Deep Compositional Spatial Models—0
A Lifting Approach to Learning-Based Self-Triggered Control with Gaussian Processes—0
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era—0
Combining Parametric Land Surface Models with Machine Learning—0
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Benchmark Results

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