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 351–360 of 1963 papers

TitleStatusHype
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models—0
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference—0
A Sparse Gaussian Process Framework for Photometric Redshift Estimation—0
Conditional Generative Modeling for Images, 3D Animations, and Video—0
Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation—0
A spectrum of physics-informed Gaussian processes for regression in engineering—0
Active learning for enumerating local minima based on Gaussian process derivatives—0
Conditional Neural Processes for Molecules—0
Conditioning of Banach Space Valued Gaussian Random Variables: An Approximation Approach Based on Martingales—0
Data-driven Force Observer for Human-Robot Interaction with Series Elastic Actuators using Gaussian Processes—0
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Benchmark Results

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