Symbolic regression is the process of identifying mathematical expressions that fit observed output from a black-box process.
Many machine learning strategies designed to automate mathematical tasks leverage neural networks to search large combinatorial spaces of mathematical symbols.
Machine learning applications to symbolic mathematics are becoming increasingly popular, yet there lacks a centralized source of real-world symbolic expressions to be used as training data.
Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence.
New forms of on-demand transportation such as ride-hailing and connected autonomous vehicles are proliferating, yet are a challenging use case for electric vehicles (EV).
To the best of our knowledge, this work is the first to consider adaptive, personalized multi-cytokine mediation therapy for sepsis, and is the first to exploit deep reinforcement learning on a biological simulation.
Characterizing a patient's progression through stages of sepsis is critical for enabling risk stratification and adaptive, personalized treatment.