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 901–910 of 1963 papers

TitleStatusHype
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning—0
Improved Inverse-Free Variational Bounds for Sparse Gaussian Processes—0
Efficient Bayesian Inference for a Gaussian Process Density Model—0
Improve in-situ life prediction and classification performance by capturing both the present state and evolution rate of battery aging—0
Efficient Approximate Inference with Walsh-Hadamard Variational Inference—0
Deep Gaussian Processes for Few-Shot Segmentation—0
Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes—0
Bayesian Quantile and Expectile Optimisation—0
Improving Random Forests by Smoothing—0
Efficient acquisition rules for model-based approximate Bayesian computation—0
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Benchmark Results

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