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 1441–1450 of 1963 papers

TitleStatusHype
Automating the Design of Multi-band Microstrip Antennas via Uniform Cross-Entropy Optimization—0
A visual exploration of Gaussian Processes and Infinite Neural Networks—0
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era—0
Bandits for Learning to Explain from Explanations—0
Band-Limited Gaussian Processes: The Sinc Kernel—0
BARK: A Fully Bayesian Tree Kernel for Black-box Optimization—0
Baryons from Mesons: A Machine Learning Perspective—0
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation—0
Bayesian active learning for choice models with deep Gaussian processes—0
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning—0
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Benchmark Results

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