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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 1311–1320 of 1963 papers

TitleStatusHype
A comparison of mixed-variables Bayesian optimization approaches—0
A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling—0
A computationally lightweight safe learning algorithm—0
Activation-level uncertainty in deep neural networks—0
Active emulation of computer codes with Gaussian processes -- Application to remote sensing—0
Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes—0
Active learning for enumerating local minima based on Gaussian process derivatives—0
Active Learning for Regression with Aggregated Outputs—0
Active Learning of Linear Embeddings for Gaussian Processes—0
Active learning of neural response functions with Gaussian processes—0
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Benchmark Results

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