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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 426–450 of 1963 papers

TitleStatusHype
Stochastic Inference of Plate Bending from Heterogeneous Data: Physics-informed Gaussian Processes via Kirchhoff-Love Theory—0
Efficient modeling of sub-kilometer surface wind with Gaussian processes and neural networks—0
Optimal Privacy-Aware Stochastic Sampling—0
Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation—0
Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processes—0
Random ReLU Neural Networks as Non-Gaussian Processes—0
A Gaussian Process Model for Ordinal Data with Applications to Chemoinformatics—0
Architectures and random properties of symplectic quantum circuits—0
Spectral complexity of deep neural networks—0
Motion Prediction with Gaussian Processes for Safe Human-Robot Interaction in Virtual Environments—0
Data-driven Force Observer for Human-Robot Interaction with Series Elastic Actuators using Gaussian Processes—0
No-Regret Learning of Nash Equilibrium for Black-Box Games via 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
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Benchmark Results

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