Abstract: Sparse Bayesian Learning (SBL) is a widely-used framework for sparse signal reconstruction, yet its standard formulation optimizes model evidence rather ...

Hyperparameter tuning is critical to the success of cross-device federated learning applications. Unfortunately, federated networks face issues of scale, heterogeneity, and privacy; addressing these ...

Machine learning models are increasingly applied across scientific disciplines, yet their effectiveness often hinges on heuristic decisions such as data transformations, training strategies, and model ...

Abstract: The growing popularity of social media platforms such as VK is accompanied by an increase in the volume of graphical digital traces left by users (for example, profile pictures or avatars).

There is a quote from this page: "Hyperparameters introduced by a regularization technique are typically nuisance hyperparameters, but whether or not we include the regularization technique at all is ...

SpaceOpt is a hyperparameter optimization algorithm that uses gradient boosting regression to find the most promising candidates for the next trial by predicting their evaluation score.