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 831–840 of 1963 papers

TitleStatusHype
Efficient Global Optimization using Deep Gaussian Processes—0
Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT—0
GPTreeO: An R package for continual regression with dividing local Gaussian processes—0
GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World Control—0
An Overview of Uncertainty Quantification Methods for Infinite Neural Networks—0
Efficient Exploration in Continuous-time Model-based Reinforcement Learning—0
Data-Efficient Interactive Multi-Objective Optimization Using ParEGO—0
Gradient-enhanced deep Gaussian processes for multifidelity modelling—0
Data Efficient Prediction of excited-state properties using Quantum Neural Networks—0
Bayesian Sparse Factor Analysis with Kernelized Observations—0
Show:102550
← PrevPage 84 of 197Next →

Benchmark Results

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