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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 961–970 of 1963 papers

TitleStatusHype
Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model Inversion—0
Distributionally Robust Optimization for Deep Kernel Multiple Instance LearningCode0
GPflux: A Library for Deep Gaussian ProcessesCode1
Uncertainty-aware Remaining Useful Life predictor—0
Adversarial Robustness Guarantees for Gaussian ProcessesCode0
Fast Design Space Exploration of Nonlinear Systems: Part I—0
Safe Online Learning-based Formation Control of Multi-Agent Systems with Gaussian Processes—0
Prediction of Ultrasonic Guided Wave Propagation in Solid-fluid and their Interface under Uncertainty using Machine Learning—0
Deep Gaussian Processes for Few-Shot Segmentation—0
Simultaneous Reconstruction and Uncertainty Quantification for Tomography—0
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Benchmark Results

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