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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 1771–1780 of 1963 papers

TitleStatusHype
Mean-Field Variational Inference for Gradient Matching with Gaussian Processes—0
Spatio-temporal Gaussian processes modeling of dynamical systems in systems biology—0
Random Feature Expansions for Deep Gaussian ProcessesCode0
Model Selection for Gaussian Process Regression by Approximation Set Coding—0
Optimizing Neural Network Hyperparameters with Gaussian Processes for Dialog Act ClassificationCode0
Appraisal of data-driven and mechanistic emulators of nonlinear hydrodynamic urban drainage simulators—0
Informative Planning and Online Learning with Sparse Gaussian Processes—0
No-Regret Replanning under Uncertainty—0
Dialogue manager domain adaptation using Gaussian process reinforcement learning—0
Using Gaussian Processes for Rumour Stance Classification in Social Media—0
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Benchmark Results

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