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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 1431–1440 of 1963 papers

TitleStatusHype
Bayesian Optimization using Deep Gaussian Processes—0
Learning Causality: Synthesis of Large-Scale Causal Networks from High-Dimensional Time Series Data—0
Deep Bayesian Convolutional Networks with Many Channels are Gaussian Processes—0
On Exact Computation with an Infinitely Wide Neural NetCode0
Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series—0
Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic FeaturesCode0
Novel Uncertainty Framework for Deep Learning Ensembles—0
Generic Variance Bounds on Estimation and Prediction Errors in Time Series Analysis: An Entropy Perspective—0
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian OptimizationCode0
Robust Deep Gaussian Processes—0
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Benchmark Results

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