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 851–860 of 1963 papers

TitleStatusHype
Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes—0
Efficient Global Optimization using Deep Gaussian Processes—0
Guided Bayesian Optimization: Data-Efficient Controller Tuning with Digital Twin—0
Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT—0
An Overview of Uncertainty Quantification Methods for Infinite Neural Networks—0
Efficient Exploration in Continuous-time Model-based Reinforcement Learning—0
Harnessing Heterogeneity: Learning from Decomposed Feedback in Bayesian Modeling—0
Heading Estimation Using Ultra-Wideband Received Signal Strength and Gaussian Processes—0
Healing Gaussian Process Experts—0
Bayesian Sparse Factor Analysis with Kernelized Observations—0
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Benchmark Results

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