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 1526–1550 of 1963 papers

TitleStatusHype
A General Framework for Fair Regression—0
Harmonizable mixture kernels with variational Fourier features—0
Non-linear process convolutions for multi-output Gaussian processes—0
Deep learning with differential Gaussian process flowsCode0
A Hybrid Approach for Trajectory Control Design—0
Deep convolutional Gaussian processesCode0
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU AccelerationCode0
Orthogonally Decoupled Variational Gaussian ProcessesCode0
Modeling longitudinal data using matrix completionCode0
Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities—0
Robustness Guarantees for Bayesian Inference with Gaussian ProcessesCode0
Learning-based attacks in cyber-physical systems—0
Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze—0
Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product NetworksCode0
Bayesian Semi-supervised Learning with Graph Gaussian ProcessesCode0
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