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 701–710 of 1963 papers

TitleStatusHype
Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches—0
A Chain Rule for the Expected Suprema of Bernoulli Processes—0
Dependence between Bayesian neural network units—0
Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning—0
Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax—0
Analysis of Brain States from Multi-Region LFP Time-Series—0
Deep Transformed Gaussian Processes—0
Bayesian Exploration of Pre-trained Models for Low-shot Image Classification—0
Deep Sigma Point Processes—0
Bayesian estimation of orientation preference maps—0
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Benchmark Results

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