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 1461–1470 of 1963 papers

TitleStatusHype
Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax—0
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC—0
Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model—0
Bayesian Inference of Log Determinants—0
Bayesian Kernelized Tensor Factorization as Surrogate for Bayesian Optimization—0
Bayesian Kernel Shaping for Learning Control—0
Bayesian Layers: A Module for Neural Network Uncertainty—0
Bayesian Learning of Dynamic Multilayer Networks—0
Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes—0
Bayesian Model Adaptation for Crowd Counts—0
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Benchmark Results

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