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 1376–1400 of 1963 papers

TitleStatusHype
An Application of Scenario Exploration to Find New Scenarios for the Development and Testing of Automated Driving Systems in Urban Scenarios—0
A New Reliable & Parsimonious Learning Strategy Comprising Two Layers of Gaussian Processes, to Address Inhomogeneous Empirical Correlation Structures—0
A New Representation of Successor Features for Transfer across Dissimilar Environments—0
An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection—0
An interpretation of the Brownian bridge as a physics-informed prior for the Poisson equation—0
Anomaly Detection and Removal Using Non-Stationary Gaussian Processes—0
A note on the smallest eigenvalue of the empirical covariance of causal Gaussian processes—0
A Novel Gaussian Min-Max Theorem and its Applications—0
A Novel Gaussian Process Based Ground Segmentation Algorithm with Local-Smoothness Estimation—0
An Overview of Uncertainty Quantification Methods for Infinite Neural Networks—0
A Perspective on Gaussian Processes for Earth Observation—0
aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture—0
Application of machine learning to gas flaring—0
Appraisal of data-driven and mechanistic emulators of nonlinear hydrodynamic urban drainage simulators—0
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs—0
Approximate Bayesian Optimisation for Neural Networks—0
Approximate Bayes learning of stochastic differential equations—0
Approximate inference in continuous time Gaussian-Jump processes—0
Approximate Sampling using an Accelerated Metropolis-Hastings based on Bayesian Optimization and Gaussian Processes—0
The Past Does Matter: Correlation of Subsequent States in Trajectory Predictions of Gaussian Process Models—0
Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC—0
Approximation-Aware Bayesian Optimization—0
Approximation errors of online sparsification criteria—0
Towards Practical Lipschitz Bandits—0
A Practitioner's Guide to Automatic Kernel Search for Gaussian Processes in Battery Applications—0
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Benchmark Results

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