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 761–770 of 1963 papers

TitleStatusHype
Partial sequence labeling with structured Gaussian Processes—0
Interrelation of equivariant Gaussian processes and convolutional neural networks—0
Kernel Learning for Explainable Climate ScienceCode0
Revisiting Active Sets for Gaussian Process DecodersCode0
Causal Modeling of Policy Interventions From Sequences of Treatments and Outcomes—0
Optimal Sensor Placement in Body Surface Networks using Gaussian Processes—0
Active learning-assisted neutron spectroscopy with log-Gaussian processes—0
Bézier Gaussian Processes for Tall and Wide Data—0
TUM sebis at GermEval 2022: A Hybrid Model Leveraging Gaussian Processes and Fine-Tuned XLM-RoBERTa for German Text Complexity AnalysisCode0
Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian ProcessesCode0
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Benchmark Results

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