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 881–890 of 1963 papers

TitleStatusHype
Deep Ensemble Kernel Learning—0
Machine Learning for Health: Personalized Models for Forecasting of Alzheimer Disease Progression—0
How to turn your camera into a perfect pinhole model—0
How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models—0
Hybrid Bayesian Neural Networks with Functional Probabilistic Layers—0
Deep Factors with Gaussian Processes for Forecasting—0
Deep Feature Gaussian Processes for Single-Scene Aerosol Optical Depth Reconstruction—0
Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models—0
Deep Gaussian Covariance Network—0
Inference on Causal Effects of Interventions in Time using Gaussian Processes—0
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Benchmark Results

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