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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 1421–1430 of 1963 papers

TitleStatusHype
A Bulirsch-Stoer algorithm using Gaussian processes—0
Learning spectrograms with convolutional spectral kernels—0
Efficient Deep Gaussian Process Models for Variable-Sized InputCode0
Forecasting Wireless Demand with Extreme Values using Feature Embedding in Gaussian Processes—0
Distribution Calibration for Regression—0
Deep Gaussian Processes with Importance-Weighted Variational InferenceCode0
Deep Neural Architecture Search with Deep Graph Bayesian OptimizationCode0
Graph Convolutional Gaussian Processes—0
Online Anomaly Detection with Sparse Gaussian Processes—0
Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational modelsCode0
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Benchmark Results

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