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 1376–1400 of 1963 papers

TitleStatusHype
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs—0
Patient-specific Conditional Joint Models of Shape, Image Features and Clinical Indicators—0
Structured Variational Inference in Unstable Gaussian Process State Space ModelsCode0
The Use of Gaussian Processes in System Identification—0
Gaussian Processes for Analyzing Positioned Trajectories in Sports—0
The Debiased Spatial Whittle LikelihoodCode0
Learning GPLVM with arbitrary kernels using the unscented transformationCode0
Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches—0
Gaussian Mixture Marginal Distributions for Modelling Remaining Pipe Wall Thickness of Critical Water Mains in Non-Destructive Evaluation—0
Spatio-thermal depth correction of RGB-D sensors based on Gaussian Processes in real-timeCode0
Modulating Surrogates for Bayesian Optimization—0
A comparison of apartment rent price prediction using a large dataset: Kriging versus DNN—0
Modeling Severe Traffic Accidents With Spatial And Temporal Features—0
Sequential Neural ProcessesCode0
Compositionally-Warped Gaussian Processes—0
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processesCode0
Multi-task Learning for Aggregated Data using Gaussian ProcessesCode0
Black-Box Inference for Non-Linear Latent Force Models—0
Multi-resolution Multi-task Gaussian ProcessesCode0
Bayesian Learning from Sequential Data using Gaussian Processes with Signature CovariancesCode0
Learning Directed Graphical Models from Gaussian Data—0
Recurrent Neural ProcessesCode0
Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective—0
CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—0
Kernelized Capsule NetworksCode0
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Benchmark Results

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