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 1476–1500 of 1963 papers

TitleStatusHype
Ensembling methods for countrywide short term forecasting of gas demand—0
Functional Regularisation for Continual Learning with Gaussian ProcessesCode0
Towards Practical Lipschitz Bandits—0
On the Limitations of Representing Functions on Sets—0
Meta-Learning Mean Functions for Gaussian Processes—0
Active Learning with Gaussian Processes for High Throughput PhenotypingCode0
Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC—0
Gaussian processes with linear operator inequality constraintsCode0
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models—0
Performance prediction of data streams on high-performance architecture—0
Learning Nonlinear State Space Models with Hamiltonian Sequential Monte Carlo Sampler—0
Multi-level CNN for lung nodule classification with Gaussian Process assisted hyperparameter optimizationCode0
Scalable GAM using sparse variational Gaussian processes—0
Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations—0
Detecting British Columbia Coastal Rainfall Patterns by Clustering Gaussian Processes—0
Recursive Estimation of Dynamic RSS Fields Based on Crowdsourcing and Gaussian Processes—0
GaussianProcesses.jl: A Nonparametric Bayes package for the Julia LanguageCode0
Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation—0
Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data—0
Evaluating the squared-exponential covariance function in Gaussian processes with integral observationsCode0
Linking Gaussian Process regression with data-driven manifold embeddings for nonlinear data fusion—0
Physics-Based Learning for Robotic Environmental Sensing—0
Bayesian Layers: A Module for Neural Network Uncertainty—0
The Limitations of Model Uncertainty in Adversarial Settings—0
Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments—0
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Benchmark Results

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