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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 771–780 of 1963 papers

TitleStatusHype
Deep Compositional Spatial Models—0
DeepCoder: Semi-parametric Variational Autoencoders for Automatic Facial Action Coding—0
Aligned Multi-Task Gaussian Process—0
Deep Bayesian Gaussian Processes for Uncertainty Estimation in Electronic Health Records—0
Deep Bayesian Convolutional Networks with Many Channels are Gaussian Processes—0
A visual exploration of Gaussian Processes and Infinite Neural Networks—0
Deep banach space kernels—0
Decoupled Sparse Gaussian Processes Components]Decoupled Sparse Gaussian Processes Components : Separating Decision Making from Data Manifold Fitting—0
Automating the Design of Multi-band Microstrip Antennas via Uniform Cross-Entropy Optimization—0
A Lifting Approach to Learning-Based Self-Triggered Control with Gaussian Processes—0
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Benchmark Results

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