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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 91–100 of 1963 papers

TitleStatusHype
Gaussian Processes for Missing Value ImputationCode1
GP-BART: a novel Bayesian additive regression trees approach using Gaussian processesCode1
Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process PriorsCode1
AutoIP: A United Framework to Integrate Physics into Gaussian ProcessesCode1
Invariance Learning in Deep Neural Networks with Differentiable Laplace ApproximationsCode1
Supervising the Multi-Fidelity Race of Hyperparameter ConfigurationsCode1
Bayesian Optimization of Function NetworksCode1
Transformers Can Do Bayesian InferenceCode1
Gaussian Process Regression With Interpretable Sample-Wise Feature WeightsCode1
State-space deep Gaussian processes with applicationsCode1
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Benchmark Results

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