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 971–980 of 1963 papers

TitleStatusHype
Bayesian Parameter Shift Rule in Variational Quantum Eigensolvers—0
A physics-informed Bayesian optimization method for rapid development of electrical machines—0
DeepRV: pre-trained spatial priors for accelerated disease mapping—0
Is SGD a Bayesian sampler? Well, almost—0
Bayesian Optimization with Tree-structured Dependencies—0
Bayesian estimation of orientation preference maps—0
A note on the smallest eigenvalue of the empirical covariance of causal Gaussian processes—0
Joint Emotion Analysis via Multi-task Gaussian Processes—0
Joint Gaussian Processes for Biophysical Parameter Retrieval—0
Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes—0
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Benchmark Results

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