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 831–840 of 1963 papers

TitleStatusHype
Damage detection in operational wind turbine blades using a new approach based on machine learning—0
A Unified Theory of Quantum Neural Network Loss Landscapes—0
Generalized Twin Gaussian Processes using Sharma-Mittal Divergence—0
GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World Control—0
Multitask Gaussian Process with Hierarchical Latent Interactions—0
Daily Land Surface Temperature Reconstruction in Landsat Cross-Track Areas Using Deep Ensemble Learning With Uncertainty Quantification—0
Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions—0
Generalization Errors and Learning Curves for Regression with Multi-task Gaussian Processes—0
DAG-GPs: Learning Directed Acyclic Graph Structure For Multi-Output Gaussian Processes—0
A Unified Kernel for Neural Network Learning—0
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Benchmark Results

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