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 621–630 of 1963 papers

TitleStatusHype
Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels—0
CAiRE\_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—0
Application of machine learning to gas flaring—0
Federated Automatic Latent Variable Selection in Multi-output Gaussian Processes—0
Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models—0
Expedited Multi-Target Search with Guaranteed Performance via Multi-fidelity Gaussian Processes—0
Experimental Data-Driven Model Predictive Control of a Hospital HVAC System During Regular Use—0
Experimentally implemented dynamic optogenetic optimization of ATPase expression using knowledge-based and Gaussian-process-supported models—0
Gaussian Process Accelerated Feldman-Cousins Approach for Physical Parameter Inference—0
Efficient modeling of sub-kilometer surface wind with Gaussian processes and neural networks—0
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Benchmark Results

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