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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 1521–1530 of 1963 papers

TitleStatusHype
A Gaussian Process perspective on Convolutional Neural Networks—0
Adversarially Robust Optimization with Gaussian Processes—0
Scalable Gaussian Processes on Discrete Domains—0
Data Association with Gaussian Processes—0
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes—0
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
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Benchmark Results

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