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 626–650 of 1963 papers

TitleStatusHype
Log-Linear-Time Gaussian Processes Using Binary Tree KernelsCode0
Temporal Knowledge Graph Completion with Approximated Gaussian Process Embedding—0
Physically Meaningful Uncertainty Quantification in Probabilistic Wind Turbine Power Curve Models as a Damage Sensitive Feature—0
Optimal Stopping with Gaussian Processes—0
Scalable Gaussian Process Hyperparameter Optimization via Coverage Regularization—0
Partial sequence labeling with structured Gaussian Processes—0
Interrelation of equivariant Gaussian processes and convolutional neural networks—0
Understanding of the properties of neural network approaches for transient light curve approximationsCode1
Kernel Learning for Explainable Climate ScienceCode0
Revisiting Active Sets for Gaussian Process DecodersCode0
Causal Modeling of Policy Interventions From Sequences of Treatments and Outcomes—0
Optimal Sensor Placement in Body Surface Networks using Gaussian Processes—0
Active learning-assisted neutron spectroscopy with log-Gaussian processes—0
TUM sebis at GermEval 2022: A Hybrid Model Leveraging Gaussian Processes and Fine-Tuned XLM-RoBERTa for German Text Complexity AnalysisCode0
Bézier Gaussian Processes for Tall and Wide Data—0
The Neural Process Family: Survey, Applications and PerspectivesCode1
Light curve completion and forecasting using fast and scalable Gaussian processes (MuyGPs)Code1
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact caseCode1
Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian ProcessesCode0
Mixtures of Gaussian Process Experts with SMC^2—0
Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations—0
Fast emulation of density functional theory simulations using approximate Gaussian processes—0
Learning linear modules in a dynamic network with missing node observations—0
Scale invariant process regression: Towards Bayesian ML with minimal assumptions—0
Modelling spatio-temporal trends of air pollution in Africa—0
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Benchmark Results

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