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 51–60 of 1963 papers

TitleStatusHype
Spatially-Heterogeneous Causal Bayesian Networks for Seismic Multi-Hazard Estimation: A Variational Approach with Gaussian Processes and Normalizing Flows—0
Sparse Gaussian Neural ProcessesCode0
Preconditioned Additive Gaussian Processes with Fourier Acceleration—0
DeepRV: pre-trained spatial priors for accelerated disease mapping—0
Stochastic Poisson Surface Reconstruction with One Solve using Geometric Gaussian Processes—0
Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems—0
A Framework for Finding Local Saddle Points in Two-Player Zero-Sum Black-Box Games—0
Informative Path Planning to Explore and Map Unknown Planetary Surfaces with Gaussian Processes—0
Disentangling Uncertainties by Learning Compressed Data RepresentationCode0
Localized Physics-informed Gaussian Processes with Curriculum Training for Topology Optimization—0
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Benchmark Results

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