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 1001–1010 of 1963 papers

TitleStatusHype
Reinforcement Learning via Gaussian Processes with Neural Network Dual Kernels—0
Remote Sensing Image Classification with Large Scale Gaussian Processes—0
Remote sensing image regression for heterogeneous change detection—0
Representing Additive Gaussian Processes by Sparse Matrices—0
Representing and Learning Functions Invariant Under Crystallographic Groups—0
Resilience of Rademacher chaos of low degree—0
Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling—0
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations—0
Review of Video Predictive Understanding: Early Action Recognition and Future Action Prediction—0
Robust and Adaptive Temporal-Difference Learning Using An Ensemble of Gaussian Processes—0
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Benchmark Results

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