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 1301–1325 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
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Benchmark Results

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