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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 881–890 of 1963 papers

TitleStatusHype
Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference—0
A brief note on understanding neural networks as Gaussian processes—0
Adaptive Inducing Points Selection For Gaussian Processes—0
A New Representation of Successor Features for Transfer across Dissimilar Environments—0
Subset-of-Data Variational Inference for Deep Gaussian-Processes RegressionCode0
Uncertainty Prediction for Machine Learning Models of Material Properties—0
Input Dependent Sparse Gaussian Processes—0
Hybrid Bayesian Neural Networks with Functional Probabilistic Layers—0
Spectrum Gaussian Processes Based On Tunable Basis Functions—0
Review of Video Predictive Understanding: Early Action Recognition and Future Action Prediction—0
Show:102550
← PrevPage 89 of 197Next →

Benchmark Results

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