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 1691–1700 of 1963 papers

TitleStatusHype
Perturbative Black Box Variational Inference—0
Analogical-based Bayesian Optimization—0
Forecasting of commercial sales with large scale Gaussian Processes—0
Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems—0
Learning from lions: inferring the utility of agents from their trajectories—0
Spectral Mixture Kernels for Multi-Output Gaussian Processes—0
Local Gaussian Processes for Efficient Fine-Grained Traffic Speed Prediction—0
An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection—0
Pillar Networks++: Distributed non-parametric deep and wide networks—0
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction—0
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Benchmark Results

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