Brenden K. Petersen
Cited by
Cited by
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
BK Petersen, ML Larma, TN Mundhenk, CP Santiago, SK Kim, JT Kim
International Conference on Learning Representations 2021, 0
Discovering symbolic policies with deep reinforcement learning
M Landajuela, BK Petersen, S Kim, CP Santiago, R Glatt, N Mundhenk, ...
International Conference on Machine Learning, 5979-5989, 2021
Symbolic regression via neural-guided genetic programming population seeding
TN Mundhenk, M Landajuela, R Glatt, CP Santiago, DM Faissol, ...
arXiv preprint arXiv:2111.00053, 2021
Deep reinforcement learning and simulation as a path toward precision medicine
BK Petersen, J Yang, WS Grathwohl, C Cockrell, C Santiago, G An, ...
Journal of Computational Biology 26 (6), 597-604, 2019
A unified framework for deep symbolic regression
M Landajuela, CS Lee, J Yang, R Glatt, CP Santiago, I Aravena, ...
Advances in Neural Information Processing Systems 35, 33985-33998, 2022
Single episode policy transfer in reinforcement learning
J Yang, B Petersen, H Zha, D Faissol
arXiv preprint arXiv:1910.07719, 2019
Symbolic regression via deep reinforcement learning enhanced genetic programming seeding
T Mundhenk, M Landajuela, R Glatt, CP Santiago, BK Petersen
Advances in Neural Information Processing Systems 34, 24912-24923, 2021
Flexible, cluster-based analysis of the electronic medical record of sepsis with composite mixture models
MB Mayhew, BK Petersen, AP Sales, JD Greene, VX Liu, TS Wasson
Journal of biomedical informatics 78, 33-42, 2018
Toward modular biological models: defining analog modules based on referent physiological mechanisms
BK Petersen, GEP Ropella, CA Hunt
BMC systems biology 8, 1-18, 2014
Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis
BK Petersen, J Yang, WS Grathwohl, C Cockrell, C Santiago, G An, ...
arXiv preprint arXiv:1802.10440, 2018
Reinforcement learning for adaptive mesh refinement
J Yang, T Dzanic, B Petersen, J Kudo, K Mittal, V Tomov, JS Camier, ...
International Conference on Artificial Intelligence and Statistics, 5997-6014, 2023
Competing mechanistic hypotheses of acetaminophen-induced hepatotoxicity challenged by virtual experiments
AK Smith, BK Petersen, GEP Ropella, RC Kennedy, N Kaplowitz, ...
PloS computational biology 12 (12), e1005253, 2016
Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning
JF Pettit, R Glatt, JR Donadee, BK Petersen
arXiv preprint arXiv:1912.03408, 2019
Improving exploration in policy gradient search: Application to symbolic optimization
M Landajuela, BK Petersen, SK Kim, CP Santiago, R Glatt, TN Mundhenk, ...
arXiv preprint arXiv:2107.09158, 2021
Virtual experiments enable exploring and challenging explanatory mechanisms of immune-mediated P450 down-regulation
BK Petersen, GEP Ropella, CA Hunt
PloS one 11 (5), e0155855, 2016
Developing a vision for executing scientifically useful virtual biomedical experiments.
BK Petersen, CA Hunt
SpringSim (ADS), 8, 2016
Distilling wikipedia mathematical knowledge into neural network models
JT Kim, M Landajuela, BK Petersen
arXiv preprint arXiv:2104.05930, 2021
An interactive visualization platform for deep symbolic regression
JT Kim, S Kim, BK Petersen
Proceedings of the Twenty-Ninth International Conference on International …, 2021
AbBERT: learning antibody humanness via masked language modeling
D Vashchenko, S Nguyen, A Goncalves, FL da Silva, B Petersen, ...
bioRxiv, 2022.08. 02.502236, 2022
Learning sparse symbolic policies for sepsis treatment
JF Pettit, BK Petersen, FL Silva, DB Larie, RC Cockrell, G An, DM Faissol
Lawrence Livermore National Lab.(LLNL), Livermore, CA (United States), 2021
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