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 1451–1460 of 1963 papers

TitleStatusHype
Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic FeaturesCode0
Generic Variance Bounds on Estimation and Prediction Errors in Time Series Analysis: An Entropy Perspective—0
Novel Uncertainty Framework for Deep Learning Ensembles—0
Robust Deep Gaussian Processes—0
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian OptimizationCode0
Generalized Variational Inference: Three arguments for deriving new PosteriorsCode0
Sentiment analysis with genetically evolved Gaussian kernels—0
A Gaussian process latent force model for joint input-state estimation in linear structural systems—0
Deep Random Splines for Point Process Intensity Estimation—0
A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes—0
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Benchmark Results

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