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 801–810 of 1963 papers

TitleStatusHype
Multitask Gaussian Process with Hierarchical Latent Interactions—0
Generalized Twin Gaussian Processes using Sharma-Mittal Divergence—0
Bayesian Variational Optimization for Combinatorial Spaces—0
A Unified Theory of Quantum Neural Network Loss Landscapes—0
Linear-time inference for Gaussian Processes on one dimension—0
Generative structured normalizing flow Gaussian processes applied to spectroscopic data—0
Gene Regulatory Network Inference with Latent Force Models—0
Generic Variance Bounds on Estimation and Prediction Errors in Time Series Analysis: An Entropy Perspective—0
Geometry-Aware Hierarchical Bayesian Learning on Manifolds—0
A computationally lightweight safe learning algorithm—0
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Benchmark Results

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