This webinar introduced healthcare researchers to Bayesian meta-analysis methods, challenging the perception that these methods are inaccessible to non-statistical researchers. The session ...

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 ...

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. In this paper, we introduce a methodology to improve upon the ...

Abstract: When training machine-learning models for brain–computer interface (BCI) decoding, practitioners often grapple with a large hyper-parameter space, where exhaustive tuning is hampered by ...

1 Department of Electrical and Computer Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan 2 School of Information Technology and Systems, University of ...

Department of Chemistry, University of Illinois at Urbana─Champaign, Urbana, Illinois 61801, United States Department of Chemistry, Rice University, Houston, Texas 77005, United States Department of ...

Add native support for Bayesian hyperparameter optimization directly within MLflow, eliminating the need for external libraries like Optuna or Hyperopt. This feature would provide a deeply integrated ...