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 1581–1590 of 1963 papers

TitleStatusHype
Bayesian Deep Learning on a Quantum ComputerCode0
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
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte CarloCode0
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
Deep Gaussian Processes with Convolutional Kernels—0
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Benchmark Results

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