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 1371–1380 of 1963 papers

TitleStatusHype
Non-Gaussian processes and neural networks at finite widths—0
Tightening Bounds for Variational Inference by Revisiting Perturbation Theory—0
Three-Dimensional Extended Object Tracking and Shape Learning Using Gaussian Processes—0
Disentangling Trainability and Generalization in Deep Learning—0
Localizing and Amortizing: Efficient Inference for Gaussian Processes—0
Kalman Filtering with Gaussian Processes Measurement Noise—0
Differentially Private Regression and Classification with Sparse Gaussian Processes—0
No-Regret Learning in Unknown Games with Correlated PayoffsCode0
Bayesian Optimisation with Gaussian Processes for Premise Selection—0
Compositional uncertainty in deep Gaussian processesCode0
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Benchmark Results

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