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 1751–1800 of 1963 papers

TitleStatusHype
Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations—0
Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning—0
Fully Scalable Gaussian Processes using Subspace Inducing Inputs—0
Functional Causal Bayesian Optimization—0
Functional Gaussian processes for regression with linear PDE models—0
Functional Priors for Bayesian Neural Networks through Wasserstein Distance Minimization to Gaussian Processes—0
Fusing Optical and SAR time series for LAI gap filling with multioutput Gaussian processes—0
Fusion of Gaussian Processes Predictions with Monte Carlo Sampling—0
Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processes—0
Gait learning for soft microrobots controlled by light fields—0
Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes—0
Gaussian behaviors: representations and data-driven control—0
Gaussian Control Barrier Functions : A Non-Parametric Paradigm to Safety—0
Gaussian-Dirichlet Random Fields for Inference over High Dimensional Categorical Observations—0
Gaussian Experts Selection using Graphical Models—0
Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processes—0
Gaussian Mixture Marginal Distributions for Modelling Remaining Pipe Wall Thickness of Critical Water Mains in Non-Destructive Evaluation—0
Gaussian Process Barrier States for Safe Trajectory Optimization and Control—0
Gaussian-Process-based Adaptive Tracking Control with Dynamic Active Learning for Autonomous Ground Vehicles—0
Gaussian process based nonlinear latent structure discovery in multivariate spike train data—0
Gaussian Process-Based Nonlinear Moving Horizon Estimation—0
Gaussian Process Classification with Privileged Information by Soft-to-Hard Labeling Transfer—0
Gaussian Process Conditional Density Estimation—0
Gaussian Process Constraint Learning for Scalable Chance-Constrained Motion Planning from Demonstrations—0
Gaussian Process Convolutional Dictionary Learning—0
Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis—0
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences—0
Gaussian Processes and Reproducing Kernels: Connections and Equivalences—0
Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces—0
Gaussian processes based data augmentation and expected signature for time series classification—0
Gaussian Processes for Analyzing Positioned Trajectories in Sports—0
Gaussian processes for Bayesian inverse problems associated with linear partial differential equations—0
Gaussian processes for dynamics learning in model predictive control—0
Gaussian Processes for Music Audio Modelling and Content Analysis—0
Gaussian Processes for Natural Language Processing—0
Gaussian Processes for Nonlinear Signal Processing—0
Gaussian Processes for Survival Analysis—0
Gaussian Processes for Traffic Speed Prediction at Different Aggregation Levels—0
Gaussian Processes indexed on the symmetric group: prediction and learning—0
Gaussian Processes in Power Systems: Techniques, Applications, and Future Works—0
Gaussian Processes on Cellular Complexes—0
Gaussian Processes on Distributions based on Regularized Optimal Transport—0
Gaussian Processes on Graphs via Spectral Kernel Learning—0
Gaussian Processes on Hypergraphs—0
Gaussian Processes Over Graphs—0
Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect—0
Gaussian Processes with Context-Supported Priors for Active Object Localization—0
Gaussian Processes with Differential Privacy—0
Gaussian Processes with Noisy Regression Inputs for Dynamical Systems—0
Gaussian Processes with State-Dependent Noise for Stochastic Control—0
Show:102550
← PrevPage 36 of 40Next →

Benchmark Results

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