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 181–190 of 1963 papers

TitleStatusHype
MOGPTK: The Multi-Output Gaussian Process ToolkitCode1
Multi-class Gaussian Process Classification with Noisy InputsCode1
Disentangling Multiple Features in Video Sequences using Gaussian Processes in Variational AutoencodersCode1
Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian ProcessesCode1
Scalable Exact Inference in Multi-Output Gaussian ProcessesCode1
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian ProcessesCode1
Sparse Orthogonal Variational Inference for Gaussian ProcessesCode1
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
Gaussian Process Optimization with Adaptive Sketching: Scalable and No RegretCode1
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)Code1
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Benchmark Results

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