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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 1011–1020 of 1963 papers

TitleStatusHype
Infinite-channel deep stable convolutional neural networks—0
Bandits for Learning to Explain from Explanations—0
Latent Map Gaussian Processes for Mixed Variable Metamodeling—0
Advanced Stationary and Non-Stationary Kernel Designs for Domain-Aware Gaussian Processes—0
Reducing the Amortization Gap in Variational Autoencoders: A Bayesian Random Function Approach—0
Estimating 2-Sinkhorn Divergence between Gaussian Processes from Finite-Dimensional Marginals—0
Gaussian Experts Selection using Graphical Models—0
A probabilistic Taylor expansion with Gaussian processes—0
Model-Based Policy Search Using Monte Carlo Gradient Estimation with Real Systems Application—0
Faster Kernel Interpolation for Gaussian Processes—0
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Benchmark Results

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