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 391–400 of 1963 papers

TitleStatusHype
Active Learning with Gaussian Processes for High Throughput PhenotypingCode0
Incorporating Sum Constraints into Multitask Gaussian ProcessesCode0
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte CarloCode0
Parameter Inference based on Gaussian Processes Informed by Nonlinear Partial Differential EquationsCode0
Inferring the Morphology of the Galactic Center Excess with Gaussian ProcessesCode0
Counterfactual Learning with Multioutput Deep KernelsCode0
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural ProcessesCode0
How Infinitely Wide Neural Networks Can Benefit from Multi-task Learning -- an Exact Macroscopic CharacterizationCode0
Active Learning with Weak Supervision for Gaussian ProcessesCode0
Efficient Large-scale Nonstationary Spatial Covariance Function Estimation Using Convolutional Neural NetworksCode0
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Benchmark Results

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