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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 251–275 of 1963 papers

TitleStatusHype
Spectral complexity of deep neural networks—0
Motion Prediction with Gaussian Processes for Safe Human-Robot Interaction in Virtual Environments—0
No-Regret Learning of Nash Equilibrium for Black-Box Games via Gaussian Processes—0
Data-driven Force Observer for Human-Robot Interaction with Series Elastic Actuators using Gaussian Processes—0
Wilsonian Renormalization of Neural Network Gaussian Processes—0
Latent Variable Double Gaussian Process Model for Decoding Complex Neural Data—0
Dynamic Online Ensembles of Basis ExpansionsCode0
Enhancing RSS-Based Visible Light Positioning by Optimal Calibrating the LED Tilt and Gain—0
Scalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks—0
Fast Evaluation of Additive Kernels: Feature Arrangement, Fourier Methods, and Kernel DerivativesCode0
Markov Chain Monte Carlo with Gaussian Process Emulation for a 1D Hemodynamics Model of CTEPH—0
COBRA -- COnfidence score Based on shape Regression Analysis for method-independent quality assessment of object pose estimation from single images—0
Neural Operator induced Gaussian Process framework for probabilistic solution of parametric partial differential equations—0
A New Reliable & Parsimonious Learning Strategy Comprising Two Layers of Gaussian Processes, to Address Inhomogeneous Empirical Correlation Structures—0
Analytical results for uncertainty propagation through trained machine learning regression models—0
BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction—0
Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes—0
Spatio-Temporal Attention and Gaussian Processes for Personalized Video Gaze EstimationCode1
Conditioning of Banach Space Valued Gaussian Random Variables: An Approximation Approach Based on Martingales—0
Universal Functional Regression with Neural Operator FlowsCode1
Tensor Network-Constrained Kernel Machines as Gaussian ProcessesCode0
Learning Piecewise Residuals of Control Barrier Functions for Safety of Switching Systems using Multi-Output Gaussian Processes—0
Multi-Agent Clarity-Aware Dynamic Coverage with Gaussian Processes—0
A Unified Kernel for Neural Network Learning—0
Guided Bayesian Optimization: Data-Efficient Controller Tuning with Digital Twin—0
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Benchmark Results

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