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 1291–1300 of 1963 papers

TitleStatusHype
Stable behaviour of infinitely wide deep neural networksCode0
Mixed Strategies for Robust Optimization of Unknown Objectives—0
Solving Dynamic Discrete Choice Models Using Smoothing and Sieve Methods—0
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process ModelsCode0
Infinitely Wide Graph Convolutional Networks: Semi-supervised Learning via Gaussian Processes—0
Knot Selection in Sparse Gaussian Processes—0
Deep Sigma Point Processes—0
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite NetworksCode0
Weakly-supervised Multi-output Regression via Correlated Gaussian Processes—0
Online Parameter Estimation for Safety-Critical Systems with Gaussian Processes—0
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Benchmark Results

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