Search Results for author: Brenden K. Petersen

Found 9 papers, 4 papers with code

Symbolic Regression via Neural-Guided Genetic Programming Population Seeding

1 code implementation29 Oct 2021 T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Claudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen

Symbolic regression is the process of identifying mathematical expressions that fit observed output from a black-box process.

Combinatorial Optimization

Improving exploration in policy gradient search: Application to symbolic optimization

1 code implementation19 Jul 2021 Mikel Landajuela Larma, Brenden K. Petersen, Soo K. Kim, Claudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol

Many machine learning strategies designed to automate mathematical tasks leverage neural networks to search large combinatorial spaces of mathematical symbols.

Incorporating domain knowledge into neural-guided search

no code implementations19 Jul 2021 Brenden K. Petersen, Claudio P. Santiago, Mikel Landajuela Larma

Many AutoML problems involve optimizing discrete objects under a black-box reward.

AutoML

Distilling Wikipedia mathematical knowledge into neural network models

no code implementations13 Apr 2021 Joanne T. Kim, Mikel Landajuela Larma, Brenden K. Petersen

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.

Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning

1 code implementation7 Dec 2019 Jacob F. Pettit, Ruben Glatt, Jonathan R. Donadee, Brenden K. Petersen

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).

Autonomous Vehicles Decision Making

Deep symbolic regression

no code implementations25 Sep 2019 Brenden K. Petersen

Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence.

Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis

no code implementations8 Feb 2018 Brenden K. Petersen, Jiachen Yang, Will S. Grathwohl, Chase Cockrell, Claudio Santiago, Gary An, Daniel M. Faissol

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.

Modeling sepsis progression using hidden Markov models

no code implementations9 Jan 2018 Brenden K. Petersen, Michael B. Mayhew, Kalvin O. E. Ogbuefi, John D. Greene, Vincent X. Liu, Priyadip Ray

Characterizing a patient's progression through stages of sepsis is critical for enabling risk stratification and adaptive, personalized treatment.

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