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 1761–1770 of 1963 papers

TitleStatusHype
Gaussian Processes for Survival Analysis—0
Stochastic Variational Deep Kernel Learning—0
Analysis of Nonstationary Time Series Using Locally Coupled Gaussian Processes—0
On Bochner's and Polya's Characterizations of Positive-Definite Kernels and the Respective Random Feature Maps—0
Personalized Risk Scoring for Critical Care Prognosis using Mixtures of Gaussian Processes—0
Learning Scalable Deep Kernels with Recurrent StructureCode0
Gaussian Process Kernels for Popular State-Space Time Series Models—0
Parallelizable sparse inverse formulation Gaussian processes (SpInGP)—0
Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation—0
Spectral Angle Based Unary Energy Functions for Spatial-Spectral Hyperspectral Classification using Markov Random Fields—0
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Benchmark Results

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