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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 1001–1010 of 1963 papers

TitleStatusHype
Measuring the robustness of Gaussian processes to kernel choice—0
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process PerspectiveCode0
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesCode0
Learning Nonparametric Volterra Kernels with Gaussian ProcessesCode0
Probabilistic Forecasting of Imbalance Prices in the Belgian Context—0
A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs—0
The Fast Kernel TransformCode0
Multi-output Gaussian Processes for Uncertainty-aware Recommender SystemsCode0
The Future is Log-Gaussian: ResNets and Their Infinite-Depth-and-Width Limit at Initialization—0
Learning particle swarming models from data with Gaussian processes—0
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Benchmark Results

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