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Explores the application of machine learning in medicine, emphasizing interpretability, variability between patients, and the quest for transparent equations in medical models.
Delves into symbolic representation of state spaces using decision diagrams for high-level Petri nets, showcasing efficient encoding techniques and benchmark results.
