SOTAVerified

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 451–460 of 1963 papers

TitleStatusHype
Combining Parametric Land Surface Models with Machine Learning—0
Deep Gaussian Processes for Few-Shot Segmentation—0
Deep Gaussian Processes for geophysical parameter retrieval—0
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation—0
Deep Gaussian Processes for Regression using Approximate Expectation Propagation—0
Deep Gaussian Processes with Convolutional Kernels—0
Deep Gaussian Processes with Decoupled Inducing Inputs—0
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning—0
Differentially Private Regression and Classification with Sparse Gaussian Processes—0
Diffusion models for Gaussian distributions: Exact solutions and Wasserstein errors—0
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Benchmark Results

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