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 1871–1880 of 1963 papers

TitleStatusHype
Hierarchical Gaussian Processes with Wasserstein-2 Kernels—0
Hierarchical Non-Stationary Temporal Gaussian Processes With L^1-Regularization—0
Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery—0
High-Dimensional Bernoulli Autoregressive Process with Long-Range Dependence—0
High-dimensional near-optimal experiment design for drug discovery via Bayesian sparse sampling—0
How to turn your camera into a perfect pinhole model—0
How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models—0
Hybrid Bayesian Neural Networks with Functional Probabilistic Layers—0
Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models—0
Hyperspectral recovery from RGB images using Gaussian Processes—0
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Benchmark Results

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