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 561–570 of 1963 papers

TitleStatusHype
Log-Gaussian Gamma Processes for Training Bayesian Neural Networks in Raman and CARS Spectroscopies—0
Infinite Width Graph Neural Networks for Node Regression/ ClassificationCode0
Consistency of some sequential experimental design strategies for excursion set estimation based on vector-valued Gaussian processes—0
Stationarity without mean reversion in improper Gaussian processes—0
Multi-Agent Bayesian Optimization with Coupled Black-Box and Affine Constraints—0
Assessment and treatment of visuospatial neglect using active learning with Gaussian processes regression—0
Leave-one-out Distinguishability in Machine LearningCode0
Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory ExplorationCode0
Tasks Makyth Models: Machine Learning Assisted Surrogates for Tipping Points—0
Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian ProcessesCode0
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Benchmark Results

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