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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 251–260 of 1963 papers

TitleStatusHype
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models—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
Bandits for Learning to Explain from Explanations—0
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era—0
All You Need is a Good Functional Prior for Bayesian Deep Learning—0
DKL-KAN: Scalable Deep Kernel Learning using Kolmogorov-Arnold Networks—0
A Lifting Approach to Learning-Based Self-Triggered Control with Gaussian Processes—0
Aligned Multi-Task Gaussian Process—0
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Benchmark Results

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