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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 651–660 of 1963 papers

TitleStatusHype
Fast Approximate Bayesian Computation for Estimating Parameters in Differential Equations—0
Clustering based on Mixtures of Sparse Gaussian Processes—0
Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression—0
A probabilistic Taylor expansion with Gaussian processes—0
Fast Design Space Exploration of Nonlinear Systems: Part I—0
Fast emulation of density functional theory simulations using approximate Gaussian processes—0
COBRA -- COnfidence score Based on shape Regression Analysis for method-independent quality assessment of object pose estimation from single images—0
Faster Kernel Interpolation for Gaussian Processes—0
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation—0
Efficiently Learning Nonstationary Gaussian Processes for Real World Impact—0
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Benchmark Results

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