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 1026–1050 of 1963 papers

TitleStatusHype
Uncertainty Disentanglement with Non-stationary Heteroscedastic Gaussian Processes for Active Learning—0
Uncertainty Informed Optimal Resource Allocation with Gaussian Process based Bayesian Inference—0
Uncertainty Prediction for Machine Learning Models of Material Properties—0
Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations—0
Uncertainty Quantification for Transformer Models for Dark-Pattern Detection—0
Uncertainty Quantification of Darcy Flow through Porous Media using Deep Gaussian Process—0
Understanding Climate Impacts on Vegetation with Gaussian Processes in Granger Causality—0
Understanding Probabilistic Sparse Gaussian Process Approximations—0
Unified field theoretical approach to deep and recurrent neuronal networks—0
Uniform Error and Posterior Variance Bounds for Gaussian Process Regression with Application to Safe Control—0
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control—0
Universal low-rank matrix recovery from Pauli measurements—0
Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGAN—0
Upgrading from Gaussian Processes to Student's-T Processes—0
Upper Trust Bound Feasibility Criterion for Mixed Constrained Bayesian Optimization with Application to Aircraft Design—0
Using BART to Perform Pareto Optimization and Quantify its Uncertainties—0
Using Contextual Information to Improve Blood Glucose Prediction—0
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes—0
Using Distance Correlation for Efficient Bayesian Optimization—0
Using Gaussian Processes for Rumour Stance Classification in Social Media—0
Using scientific machine learning for experimental bifurcation analysis of dynamic systems—0
V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures—0
Value-at-Risk Optimization with Gaussian Processes—0
Variable noise and dimensionality reduction for sparse Gaussian processes—0
Variance based sensitivity analysis for Monte Carlo and importance sampling reliability assessment with Gaussian processes—0
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Benchmark Results

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