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 1541–1550 of 1963 papers

TitleStatusHype
Efficient Global Optimization using Deep Gaussian Processes—0
Gait learning for soft microrobots controlled by light fields—0
Non-Parametric Variational Inference with Graph Convolutional Networks for Gaussian Processes—0
Hands-on Experience with Gaussian Processes (GPs): Implementing GPs in Python - I—0
Physically-Inspired Gaussian Process Models for Post-Transcriptional Regulation in DrosophilaCode0
Inter-state switching in stochastic gene expression: Exact solution, an adiabatic limit and oscillations in molecular distributions—0
Learning Invariances using the Marginal Likelihood—0
Deep Convolutional Networks as shallow Gaussian ProcessesCode0
Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing—0
Multi-Output Convolution Spectral Mixture for Gaussian Processes—0
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Benchmark Results

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