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 81–90 of 1963 papers

TitleStatusHype
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian ProcessesCode1
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical SimulationsCode1
A tutorial on learning from preferences and choices with Gaussian ProcessesCode1
Deep Kernel LearningCode1
DeepKriging: Spatially Dependent Deep Neural Networks for Spatial PredictionCode1
Deep Pipeline Embeddings for AutoMLCode1
Dense Gaussian Processes for Few-Shot SegmentationCode1
Differentiable Compositional Kernel Learning for Gaussian ProcessesCode1
Disentangling Derivatives, Uncertainty and Error in Gaussian Process ModelsCode1
A Unifying Variational Framework for Gaussian Process Motion PlanningCode1
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Benchmark Results

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