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 551–575 of 1963 papers

TitleStatusHype
BrowNNe: Brownian Nonlocal Neurons & Activation Functions—0
Branching Gaussian Processes with Applications to Spatiotemporal Reconstruction of 3D Trees—0
A Bayesian take on option pricing with Gaussian processes—0
BOP-Elites, a Bayesian Optimisation algorithm for Quality-Diversity search—0
Entry Dependent Expert Selection in Distributed Gaussian Processes Using Multilabel Classification—0
Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration—0
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models—0
Extended and Unscented Gaussian Processes—0
BOIS: Bayesian Optimization of Interconnected Systems—0
Blitzkriging: Kronecker-structured Stochastic Gaussian Processes—0
Approximate inference in continuous time Gaussian-Jump processes—0
Approximate Bayes learning of stochastic differential equations—0
Bivariate DeepKriging for Large-scale Spatial Interpolation of Wind Fields—0
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks—0
Experimental Data-Driven Model Predictive Control of a Hospital HVAC System During Regular Use—0
BI-EqNO: Generalized Approximate Bayesian Inference with an Equivariant Neural Operator Framework—0
Approximate Bayesian Optimisation for Neural Networks—0
Activation-level uncertainty in deep neural networks—0
Bézier Gaussian Processes for Tall and Wide Data—0
Bézier Curve Gaussian Processes—0
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs—0
Bayesian Optimization using Deep Gaussian Processes—0
Experimentally implemented dynamic optogenetic optimization of ATPase expression using knowledge-based and Gaussian-process-supported models—0
Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei—0
Appraisal of data-driven and mechanistic emulators of nonlinear hydrodynamic urban drainage simulators—0
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Benchmark Results

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