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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 481–490 of 1963 papers

TitleStatusHype
Posterior Inference on Shallow Infinitely Wide Bayesian Neural Networks under Weights with Unbounded VarianceCode0
Dynamic Term Structure Models with Nonlinearities using Gaussian Processes—0
Physics Inspired Approaches To Understanding Gaussian ProcessesCode1
Quantum neural networks form Gaussian processes—0
Learning Switching Port-Hamiltonian Systems with Uncertainty Quantification—0
Meta-models for transfer learning in source localisation—0
NUBO: A Transparent Python Package for Bayesian OptimizationCode1
Disentangled Multi-Fidelity Deep Bayesian Active LearningCode1
Optimizing Hyperparameters with Conformal Quantile RegressionCode1
Representing Additive Gaussian Processes by Sparse Matrices—0
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Benchmark Results

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