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 221–230 of 1963 papers

TitleStatusHype
Aggregation Models with Optimal Weights for Distributed Gaussian Processes—0
Amortized Bayesian Local Interpolation NetworK: Fast covariance parameter estimation for Gaussian Processes—0
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning—0
Bayesian active learning for choice models with deep Gaussian processes—0
A Meta-Learning Approach to Population-Based Modelling of Structures—0
Adaptive finite element type decomposition of Gaussian processes—0
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation—0
A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes—0
Baryons from Mesons: A Machine Learning Perspective—0
A Machine Consciousness architecture based on Deep Learning and Gaussian Processes—0
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Benchmark Results

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