Abstract: For distributed-drive electric vehicles, torque vectoring control based on model predictive control (MPC) has emerged as a preferred strategy to achieve superior performance across diverse ...

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 ...

A modular and production-ready toolkit for evaluating machine learning models using accuracy, precision, recall, F1-score, and cross-validation. Includes advanced hyperparameter tuning (GridSearchCV, ...

Abstract: This paper investigates the impact of reward functions, action space configurations, and hyperparameter tuning on AWS DeepRacer models for autonomous driving. Our experiments compare ...

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.