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 431–440 of 1963 papers

TitleStatusHype
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decompositionCode1
Improve in-situ life prediction and classification performance by capturing both the present state and evolution rate of battery aging—0
Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes—0
Federated Causal Inference from Observational DataCode0
Fast Risk Assessment in Power Grids through Novel Gaussian Process and Active Learning—0
Gaussian Process Regression for Maximum Entropy Distribution—0
Emerging Statistical Machine Learning Techniques for Extreme Temperature Forecasting in U.S. Cities—0
Learning-based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy—0
Mode-constrained Model-based Reinforcement Learning via Gaussian ProcessesCode0
Current Methods for Drug Property Prediction in the Real World—0
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Benchmark Results

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