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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 1021–1030 of 1963 papers

TitleStatusHype
Hierarchical Non-Stationary Temporal Gaussian Processes With L^1-Regularization—0
Probabilistic Robust Linear Quadratic Regulators with Gaussian ProcessesCode0
Priors in Bayesian Deep Learning: A Review—0
Value-at-Risk Optimization with Gaussian Processes—0
Deep Neural Networks as Point Estimates for Deep Gaussian Processes—0
SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data—0
Laplace Matching for fast Approximate Inference in Latent Gaussian ModelsCode0
Normal Tempered Stable Processes and the Pricing of Energy Derivatives—0
Local approximate Gaussian process regression for data-driven constitutive laws: Development and comparison with neural networks—0
Practical and Rigorous Uncertainty Bounds for Gaussian Process RegressionCode0
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Benchmark Results

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