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 1511–1520 of 1963 papers

TitleStatusHype
Physics-Based Learning for Robotic Environmental Sensing—0
Bayesian Layers: A Module for Neural Network Uncertainty—0
The Limitations of Model Uncertainty in Adversarial Settings—0
Temporal alignment and latent Gaussian process factor inference in population spike trains—0
Inferring Latent Velocities from Weather Radar Data using Gaussian Processes—0
Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments—0
Deep Factors with Gaussian Processes for Forecasting—0
Neural Non-Stationary Spectral KernelCode0
Sequence Alignment with Dirichlet Process Mixtures—0
Robust Super-Level Set Estimation using Gaussian Processes—0
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Benchmark Results

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