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 26–50 of 1963 papers

TitleStatusHype
Gaussian process-based online health monitoring and fault analysis of lithium-ion battery systems from field dataCode1
A Rate-Distortion View of Uncertainty QuantificationCode1
Spatio-Temporal Attention and Gaussian Processes for Personalized Video Gaze EstimationCode1
Universal Functional Regression with Neural Operator FlowsCode1
A tutorial on learning from preferences and choices with Gaussian ProcessesCode1
Multi-Fidelity Residual Neural Processes for Scalable Surrogate ModelingCode1
Exact, Fast and Expressive Poisson Point Processes via Squared Neural FamiliesCode1
Standard Gaussian Process Can Be Excellent for High-Dimensional Bayesian OptimizationCode1
Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian ProcessesCode1
Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage GuaranteesCode1
GP+: A Python Library for Kernel-based learning via Gaussian ProcessesCode1
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical SimulationsCode1
Sound field reconstruction using neural processes with dynamic kernelsCode1
Solving High Frequency and Multi-Scale PDEs with Gaussian ProcessesCode1
Stochastic Gradient Descent for Gaussian Processes Done RightCode1
Thin and Deep Gaussian ProcessesCode1
A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics IdentificationCode1
Implicit Gaussian process representation of vector fields over arbitrary latent manifoldsCode1
Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space ModelsCode1
A Unifying Variational Framework for Gaussian Process Motion PlanningCode1
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decompositionCode1
GaPro: Box-Supervised 3D Point Cloud Instance Segmentation Using Gaussian Processes as Pseudo LabelersCode1
SEAL: Simultaneous Exploration and Localization in Multi-Robot SystemsCode1
Sampling from Gaussian Process Posteriors using Stochastic Gradient DescentCode1
Memory-Based Dual Gaussian Processes for Sequential LearningCode1
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Benchmark Results

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