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 1041–1050 of 1963 papers

TitleStatusHype
Convolutional Normalizing Flows for Deep Gaussian Processes—0
Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model Inversion—0
Distributionally Robust Optimization for Deep Kernel Multiple Instance LearningCode0
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
Deep Gaussian Processes for Few-Shot Segmentation—0
Prediction of Ultrasonic Guided Wave Propagation in Solid-fluid and their Interface under Uncertainty using Machine Learning—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