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 1191–1200 of 1963 papers

TitleStatusHype
Fast Deep Mixtures of Gaussian Process Experts—0
Scalable Partial Explainability in Neural Networks via Flexible Activation Functions—0
Syn2Real Transfer Learning for Image Deraining using Gaussian ProcessesCode1
Variational Auto-Regressive Gaussian Processes for Continual LearningCode1
Smart Forgetting for Safe Online Learning with Gaussian Processes—0
Learning supported Model Predictive Control for Tracking of Periodic References—0
Regret Bound for Safe Gaussian Process Bandit Optimization—0
Learning Constrained Dynamics with Gauss' Principle adhering Gaussian ProcessesCode0
Multi-Fidelity High-Order Gaussian Processes for Physical SimulationCode0
Physics Informed Deep Kernel Learning—0
Show:102550
← PrevPage 120 of 197Next →

Benchmark Results

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