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 1061–1070 of 1963 papers

TitleStatusHype
Scalable Multi-Class Gaussian Process Classification using Expectation Propagation—0
Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference—0
Scalable Multi-Task Gaussian Processes with Neural Embedding of Coregionalization—0
Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes—0
Scalable Partial Explainability in Neural Networks via Flexible Activation Functions—0
Scalable Uncertainty for Computer Vision with Functional Variational Inference—0
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO—0
Scale invariant process regression: Towards Bayesian ML with minimal assumptions—0
Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure—0
Scaling Gaussian Processes with Derivative Information Using Variational Inference—0
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Benchmark Results

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