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 1511–1520 of 1963 papers

TitleStatusHype
Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems—0
Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection—0
Clustering based on Mixtures of Sparse Gaussian Processes—0
Coarse-scale PDEs from fine-scale observations via machine learning—0
COBRA -- COnfidence score Based on shape Regression Analysis for method-independent quality assessment of object pose estimation from single images—0
Collaborative Gaussian Processes for Preference Learning—0
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems—0
Combining additivity and active subspaces for high-dimensional Gaussian process modeling—0
Combining Gaussian processes and polynomial chaos expansions for stochastic nonlinear model predictive control—0
Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development—0
Show:102550
← PrevPage 152 of 197Next →

Benchmark Results

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