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 701–710 of 1963 papers

TitleStatusHype
Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation—0
Bayesian Active Learning with Fully Bayesian Gaussian ProcessesCode1
Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels—0
Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibilityCode0
An Application of Scenario Exploration to Find New Scenarios for the Development and Testing of Automated Driving Systems in Urban Scenarios—0
High-dimensional additive Gaussian processes under monotonicity constraintsCode1
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite KernelCode0
Modelling stellar activity with Gaussian process regression networksCode0
Hyper-parameter tuning of physics-informed neural networks: Application to Helmholtz problemsCode0
Probabilistic Estimation of Instantaneous Frequencies of Chirp SignalsCode1
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Benchmark Results

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