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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 1676–1700 of 1963 papers

TitleStatusHype
Multi-Fidelity High-Order Gaussian Processes for Physical SimulationCode0
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical ApplicationsCode0
Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive OrdersCode0
Data-driven Aerodynamic Analysis of Structures using Gaussian ProcessesCode0
Multi-Instance Partial-Label Learning: Towards Exploiting Dual Inexact SupervisionCode0
Multi-level CNN for lung nodule classification with Gaussian Process assisted hyperparameter optimizationCode0
Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial LibrariesCode0
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite NetworksCode0
Raven's Progressive Matrices Completion with Latent Gaussian Process PriorsCode0
Generalized Variational Inference: Three arguments for deriving new PosteriorsCode0
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep LearningCode0
Automatic Construction and Natural-Language Description of Nonparametric Regression ModelsCode0
SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with DerivativesCode0
Multi-output Gaussian Processes for Uncertainty-aware Recommender SystemsCode0
Multi-Output Gaussian Processes for Graph-Structured DataCode0
Variational zero-inflated Gaussian processes with sparse kernelsCode0
Multioutput Gaussian Processes with Functional Data: A Study on Coastal Flood Hazard AssessmentCode0
Global Convolutional Neural ProcessesCode0
Dynamic Bayesian Learning for Spatiotemporal Mechanistic ModelsCode0
Bayesian Structured Prediction Using Gaussian ProcessesCode0
A Gaussian Process-based Streaming Algorithm for Prediction of Time Series With Regimes and OutliersCode0
Multi-resolution Multi-task Gaussian ProcessesCode0
Global Safe Sequential Learning via Efficient Knowledge TransferCode0
Global universal approximation of functional input maps on weighted spacesCode0
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process ModelsCode0
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Benchmark Results

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