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 831–840 of 1963 papers

TitleStatusHype
Probabilistic Models for Manufacturing Lead Times—0
Know Thy Student: Interactive Learning with Gaussian Processes—0
Local Gaussian process extrapolation for BART models with applications to causal inference—0
Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGAN—0
A piece-wise constant approximation for non-conjugate Gaussian Process modelsCode0
Inducing Gaussian Process Networks—0
Active Learning with Weak Supervision for Gaussian ProcessesCode0
PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations—0
Discovering and forecasting extreme events via active learning in neural operators—0
Autoencoder Attractors for Uncertainty EstimationCode0
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Benchmark Results

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