2 code implementations • 29 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.
no code implementations • 19 Jul 2021 • Brenden K. Petersen, Claudio P. Santiago, Mikel Landajuela Larma
Many AutoML problems involve optimizing discrete objects under a black-box reward.
1 code implementation • 19 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.
no code implementations • 13 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.
1 code implementation • ICLR 2021 • Brenden K. Petersen, Mikel Landajuela Larma, T. Nathan Mundhenk, Claudio P. Santiago, Soo K. Kim, Joanne T. Kim
Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence.
1 code implementation • 7 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).
no code implementations • 25 Sep 2019 • Brenden K. Petersen
Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence.
no code implementations • 8 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.
no code implementations • 9 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.