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 751–775 of 1963 papers

TitleStatusHype
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation—0
Deep Gaussian Processes for Few-Shot Segmentation—0
Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model Inversion—0
A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes—0
Deep Gaussian Processes: A Survey—0
Deep Gaussian Processes—0
Baryons from Mesons: A Machine Learning Perspective—0
A Machine Consciousness architecture based on Deep Learning and Gaussian Processes—0
Adaptive finite element type decomposition of Gaussian processes—0
Accurate and Uncertainty-Aware Multi-Task Prediction of HEA Properties Using Prior-Guided Deep Gaussian Processes—0
Aggregation Models with Optimal Weights for Distributed Gaussian Processes—0
BARK: A Fully Bayesian Tree Kernel for Black-box Optimization—0
Deep Gaussian Covariance Network—0
Deep Feature Gaussian Processes for Single-Scene Aerosol Optical Depth Reconstruction—0
Band-Limited Gaussian Processes: The Sinc Kernel—0
Deep Factors with Gaussian Processes for Forecasting—0
Bandits for Learning to Explain from Explanations—0
Deep Ensemble Kernel Learning—0
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era—0
All You Need is a Good Functional Prior for Bayesian Deep Learning—0
Deep Compositional Spatial Models—0
DeepCoder: Semi-parametric Variational Autoencoders for Automatic Facial Action Coding—0
Aligned Multi-Task Gaussian Process—0
Deep Bayesian Gaussian Processes for Uncertainty Estimation in Electronic Health Records—0
Deep Bayesian Convolutional Networks with Many Channels are Gaussian Processes—0
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Benchmark Results

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