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 461–470 of 1963 papers

TitleStatusHype
Combining Parametric Land Surface Models with Machine Learning—0
Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An application to high-throughput toxicity testing—0
Dirichlet Logistic Gaussian Processes for Evaluation of Black-Box Stochastic Systems under Complex Requirements—0
Deep kernel processes—0
Discriminative training for Convolved Multiple-Output Gaussian processes—0
Disentangling Trainability and Generalization in Deep Learning—0
Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics—0
Deep learning generalizes because the parameter-function map is biased towards simple functions—0
Bayesian approach to model-based extrapolation of nuclear observables—0
Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development—0
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Benchmark Results

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