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Found 9 papers, 4 papers with code
Modeling sepsis progression using hidden Markov models
Characterizing a patient's progression through stages of sepsis is critical for enabling risk stratification and adaptive, personalized treatment.
Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis
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.
Deep symbolic regression
Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence.
Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning
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).
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence.
Distilling Wikipedia mathematical knowledge into neural network models
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.
Incorporating domain knowledge into neural-guided search
Many AutoML problems involve optimizing discrete objects under a black-box reward.
Improving exploration in policy gradient search: Application to symbolic optimization
Many machine learning strategies designed to automate mathematical tasks leverage neural networks to search large combinatorial spaces of mathematical symbols.
Symbolic Regression via Neural-Guided Genetic Programming Population Seeding
Symbolic regression is the process of identifying mathematical expressions that fit observed output from a black-box process.
