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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 901–925 of 1963 papers

TitleStatusHype
Gaussian Process Pseudo-Likelihood Models for Sequence Labeling—0
Improved Inverse-Free Variational Bounds for Sparse Gaussian Processes—0
Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies—0
Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder—0
Convolutional Normalizing Flows for Deep Gaussian Processes—0
A Tutorial on Sparse Gaussian Processes and Variational Inference—0
Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes—0
A generalised form for a homogeneous population of structures using an overlapping mixture of Gaussian processes—0
Gaussian Process Optimization with Mutual Information—0
Gaussian Process on the Product of Directional Manifolds—0
Gaussian Process Neurons Learn Stochastic Activation Functions—0
Gaussian Process Neurons—0
Incremental Ensemble Gaussian Processes—0
Incremental Learning of Motion Primitives for Pedestrian Trajectory Prediction at Intersections—0
Incremental Structure Discovery of Classification via Sequential Monte Carlo—0
Index Set Fourier Series Features for Approximating Multi-dimensional Periodic Kernels—0
Gaussian Process Morphable Models—0
Gaussian Process Molecule Property Prediction with FlowMO—0
Gaussian Process Models of Sound Change in Indo-Aryan Dialectology—0
Inference at the data's edge: Gaussian processes for modeling and inference under model-dependency, poor overlap, and extrapolation—0
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds—0
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds—0
A Tucker decomposition process for probabilistic modeling of diffusion magnetic resonance imaging—0
A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes—0
Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders—0
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Benchmark Results

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