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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 601–625 of 1963 papers

TitleStatusHype
Mode-constrained Model-based Reinforcement Learning via Gaussian ProcessesCode0
Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT—0
Amortized Variational Inference: When and Why?Code0
Investigating Low Data, Confidence Aware Image Prediction on Smooth Repetitive Videos using Gaussian Processes—0
Towards a population-informed approach to the definition of data-driven models for structural dynamics—0
Gaussian processes for Bayesian inverse problems associated with linear partial differential equations—0
Bivariate DeepKriging for Large-scale Spatial Interpolation of Wind Fields—0
Flexible and efficient emulation of spatial extremes processes via variational autoencodersCode0
Beyond Intuition, a Framework for Applying GPs to Real-World DataCode0
Efficient Determination of Safety Requirements for Perception Systems—0
Uncertainty Informed Optimal Resource Allocation with Gaussian Process based Bayesian Inference—0
Spatiotemporal Besov Priors for Bayesian Inverse Problems—0
Evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems—0
Time-Varying Transition Matrices with Multi-task Gaussian Processes—0
Spatio-temporal DeepKriging for Interpolation and Probabilistic Forecasting—0
A Bayesian Take on Gaussian Process NetworksCode0
Efficient Large-scale Nonstationary Spatial Covariance Function Estimation Using Convolutional Neural NetworksCode0
Amortized Inference for Gaussian Process Hyperparameters of Structured KernelsCode0
Functional Causal Bayesian Optimization—0
Monte Carlo inference for semiparametric Bayesian regression—0
Representing and Learning Functions Invariant Under Crystallographic Groups—0
Training-Free Neural Active Learning with Initialization-Robustness GuaranteesCode0
Graph Classification Gaussian Processes via Spectral Features—0
Vehicle Dynamics Modeling for Autonomous Racing Using Gaussian Processes—0
Global universal approximation of functional input maps on weighted spacesCode0
Show:102550
← PrevPage 25 of 79Next →

Benchmark Results

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