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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1301–1350 of 1963 papers

TitleStatusHype
A brief note on understanding neural networks as Gaussian processes—0
A Bulirsch-Stoer algorithm using Gaussian processes—0
Accelerating ABC methods using Gaussian processes—0
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes—0
Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty—0
Accurate and Uncertainty-Aware Multi-Task Prediction of HEA Properties Using Prior-Guided Deep Gaussian Processes—0
ASMCNN: An Efficient Brain Extraction Using Active Shape Model and Convolutional Neural Networks—0
A chain rule for the expected suprema of Gaussian processes—0
A Chain Rule for the Expected Suprema of Bernoulli Processes—0
A comparison of apartment rent price prediction using a large dataset: Kriging versus DNN—0
A comparison of mixed-variables Bayesian optimization approaches—0
A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling—0
A computationally lightweight safe learning algorithm—0
Activation-level uncertainty in deep neural networks—0
Active emulation of computer codes with Gaussian processes -- Application to remote sensing—0
Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes—0
Active learning for enumerating local minima based on Gaussian process derivatives—0
Active Learning for Regression with Aggregated Outputs—0
Active Learning of Linear Embeddings for Gaussian Processes—0
Active learning of neural response functions with Gaussian processes—0
Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data—0
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models—0
Adaptive finite element type decomposition of Gaussian processes—0
Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets—0
Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models—0
Adaptive Inducing Points Selection For Gaussian Processes—0
Adaptive Low-Pass Filtering using Sliding Window Gaussian Processes—0
Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches—0
Adaptive Sensing for Learning Nonstationary Environment Models—0
A Data-Driven Gaussian Process Filter for Electrocardiogram Denoising—0
A dependent partition-valued process for multitask clustering and time evolving network modelling—0
A Driver Behavior Modeling Structure Based on Non-parametric Bayesian Stochastic Hybrid Architecture—0
Advanced Stationary and Non-Stationary Kernel Designs for Domain-Aware Gaussian Processes—0
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks—0
Adversarially Robust Optimization with Gaussian Processes—0
A Dynamic Programming Algorithm for Finding an Optimal Sequence of Informative Measurements—0
A theory of representation learning gives a deep generalisation of kernel methods—0
A flexible state space model for learning nonlinear dynamical systems—0
A Framework for Finding Local Saddle Points in Two-Player Zero-Sum Black-Box Games—0
A Fully-Automated Framework Integrating Gaussian Process Regression and Bayesian Optimization to Design Pin-Fins—0
A Gaussian Process Based Method with Deep Kernel Learning for Pricing High-dimensional American Options—0
A Gaussian process latent force model for joint input-state estimation in linear structural systems—0
A Gaussian Process Model for Ordinal Data with Applications to Chemoinformatics—0
Correlated Dynamics in Marketing Sensitivities—0
A Gaussian Process perspective on Convolutional Neural Networks—0
A Gaussian Process Regression based Dynamical Models Learning Algorithm for Target Tracking—0
A Gaussian Process Regression Model for Distribution Inputs—0
A General Framework for Fair Regression—0
A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes—0
A generalised form for a homogeneous population of structures using an overlapping mixture of Gaussian processes—0
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Benchmark Results

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