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
Convolutional Normalizing Flows for Deep Gaussian Processes—0
Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies—0
Correcting Model Bias with Sparse Implicit Processes—0
Correlated Product of Experts for Sparse Gaussian Process Regression—0
Correlational Gaussian Processes for Cross-Domain Visual Recognition—0
AUGUR, A flexible and efficient optimization algorithm for identification of optimal adsorption sites—0
A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes—0
DADEE: Well-calibrated uncertainty quantification in neural networks for barriers-based robot safety—0
DAG-GPs: Learning Directed Acyclic Graph Structure For Multi-Output Gaussian Processes—0
A brief note on understanding neural networks as Gaussian processes—0
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Benchmark Results

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