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 121–130 of 1963 papers

TitleStatusHype
Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and RegressionCode0
Adaptive Sampling to Reduce Epistemic Uncertainty Using Prediction Interval-Generation Neural NetworksCode0
Bayesian Optimization via Continual Variational Last Layer Training—0
Data Efficient Prediction of excited-state properties using Quantum Neural Networks—0
Dimensionality Reduction Techniques for Global Bayesian Optimisation—0
Epidemiological Model Calibration via Graybox Bayesian Optimization—0
Uncertainty Quantification for Transformer Models for Dark-Pattern Detection—0
Nonmyopic Global Optimisation via Approximate Dynamic ProgrammingCode0
Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep LearningCode0
Gaussian Processes for Probabilistic Estimates of Earthquake Ground Shaking: A 1-D Proof-of-ConceptCode0
Show:102550
← PrevPage 13 of 197Next →

Benchmark Results

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