SOTAVerified

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 721–730 of 1963 papers

TitleStatusHype
A Bayesian Approach for Shaft Centre Localisation in Journal Bearings—0
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification—0
Deep Neural Networks as Point Estimates for Deep Gaussian Processes—0
Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments—0
Amortized Variational Inference for Deep Gaussian Processes—0
Bayesian Complementary Kernelized Learning for Multidimensional Spatiotemporal Data—0
Meta-Learning Mean Functions for Gaussian Processes—0
Amortized variance reduction for doubly stochastic objectives—0
Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models—0
Deep Manifold Prior—0
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Benchmark Results

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