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 491–500 of 1963 papers

TitleStatusHype
Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation—0
A Framework for Finding Local Saddle Points in Two-Player Zero-Sum Black-Box Games—0
Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning—0
Dependence between Bayesian neural network units—0
Designing Robust Biotechnological Processes Regarding Variabilities using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design—0
Using Gaussian Processes to Design Dynamic Experiments for Black-Box Model Discrimination under Uncertainty—0
Design of Experiments for Verifying Biomolecular Networks—0
Detecting British Columbia Coastal Rainfall Patterns by Clustering Gaussian Processes—0
Analysis of Nonstationary Time Series Using Locally Coupled Gaussian Processes—0
Collaborative Gaussian Processes for Preference Learning—0
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Benchmark Results

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