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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 1571–1580 of 1963 papers

TitleStatusHype
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees—0
Neural-net-induced Gaussian process regression for function approximation and PDE solution—0
Stagewise Safe Bayesian Optimization with Gaussian Processes—0
Neural Tangent Kernel: Convergence and Generalization in Neural NetworksCode1
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte CarloCode0
Differentiable Compositional Kernel Learning for Gaussian ProcessesCode1
Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty—0
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces—0
Grouped Gaussian Processes for Solar Power Prediction—0
Variational Implicit ProcessesCode0
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Benchmark Results

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