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 881–890 of 1963 papers

TitleStatusHype
How Infinitely Wide Neural Networks Can Benefit from Multi-task Learning -- an Exact Macroscopic CharacterizationCode0
When are Iterative Gaussian Processes Reliably Accurate?Code0
Rough multifactor volatility for SPX and VIX options—0
GPEX, A Framework For Interpreting Artificial Neural NetworksCode0
Learning-based methods to model small body gravity fields for proximity operations: Safety and Robustness—0
Correlated Product of Experts for Sparse Gaussian Process Regression—0
Modeling Advection on Directed Graphs using Matérn Gaussian Processes for Traffic Flow—0
Experimental Data-Driven Model Predictive Control of a Hospital HVAC System During Regular Use—0
Learning Rigidity-based Flocking Control with Gaussian Processes—0
A Sparse Expansion For Deep Gaussian Processes—0
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Benchmark Results

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