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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 181–190 of 1963 papers

TitleStatusHype
Non-Gaussian Gaussian Processes for Few-Shot RegressionCode1
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical SimulationsCode1
Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical ReactorsCode1
Optimizing Hyperparameters with Conformal Quantile RegressionCode1
Pathwise Conditioning of Gaussian ProcessesCode1
Personalized Federated Learning with Gaussian ProcessesCode1
Positional Encoder Graph Neural Networks for Geographic DataCode1
Posterior and Computational Uncertainty in Gaussian ProcessesCode1
Probabilistic Estimation of Instantaneous Frequencies of Chirp SignalsCode1
Kernel Methods and their derivatives: Concept and perspectives for the Earth system sciencesCode1
Show:102550
← PrevPage 19 of 197Next →

Benchmark Results

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