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 211–220 of 1963 papers

TitleStatusHype
Adaptive Inducing Points Selection For Gaussian Processes—0
Bayesian Optimization by Kernel Regression and Density-based Exploration—0
Bayesian optimization explains human active search—0
Bayesian Optimization of Bilevel Problems—0
Amortized Variational Inference for Deep Gaussian Processes—0
Amortized variance reduction for doubly stochastic objectives—0
Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models—0
Amortized Safe Active Learning for Real-Time Data Acquisition: Pretrained Neural Policies from Simulated Nonparametric Functions—0
Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets—0
ASMCNN: An Efficient Brain Extraction Using Active Shape Model and Convolutional Neural Networks—0
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Benchmark Results

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