Kamu erişimi zorunlu olan makaleler - Mikel LandajuelaDaha fazla bilgi edinin
Bir yerde sunuluyor: 13
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
Zorunlu olanlar: US Department of Energy
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
Zorunlu olanlar: US Department of Energy
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
Zorunlu olanlar: US Department of Energy
Numerical approximation of the electromechanical coupling in the left ventricle with inclusion of the Purkinje network
M Landajuela, C Vergara, A Gerbi, L Dedè, L Formaggia, A Quarteroni
International journal for numerical methods in biomedical engineering 34 (7 …, 2018
Zorunlu olanlar: European Commission, Government of Italy
SRBench++: Principled benchmarking of symbolic regression with domain-expert interpretation
FO de Franca, M Virgolin, M Kommenda, MS Majumder, M Cranmer, ...
IEEE transactions on evolutionary computation, 2024
Zorunlu olanlar: Fundação para a Ciência e a Tecnologia, Portugal
Incorporating domain knowledge into neural-guided search via in situ priors and constraints
BK Petersen, CP Santiago, M Landajuela
Lawrence Livermore National Lab.(LLNL), Livermore, CA (United States), 2021
Zorunlu olanlar: US Department of Energy
Computationally restoring the potency of a clinical antibody against Omicron
TA Desautels, KT Arrildt, AT Zemla, EY Lau, F Zhu, D Ricci, S Cronin, ...
Nature 629 (8013), 878-885, 2024
Zorunlu olanlar: Howard Hughes Medical Institute
Leveraging language models to efficiently learn symbolic optimization solutions
FL da Silva, A Goncalves, S Nguyen, D Vashchenko, R Glatt, T Desautels, ...
Adaptive and Learning Agents (ALA) Workshop at AAMAS, 2022
Zorunlu olanlar: US Department of Energy
Language model-accelerated deep symbolic optimization
FL da Silva, A Goncalves, S Nguyen, D Vashchenko, R Glatt, T Desautels, ...
Neural Computing and Applications, 1-17, 2023
Zorunlu olanlar: US Department of Energy
Toward multi-fidelity reinforcement learning for symbolic optimization
FL Silva, J Yang, M Landajuela, A Goncalves, A Ladd, D Faissol, ...
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States), 2023
Zorunlu olanlar: US Department of Energy
Pareto-Front Training for Multi-objective Symbolic Optimization
JG Faris, CF Hayes, A Goncalves, KG Sprenger, D Faissol, B Petersen, ...
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States), 2023
Zorunlu olanlar: US Department of Energy, US Department of Defense
Splitting schemes and unfitted-mesh methods for the coupling of an incompressible fluid with a thin-walled structure
MA Fernández, M Landajuela
IMA Journal of Numerical Analysis 40 (2), 1407-1453, 2020
Zorunlu olanlar: Agence Nationale de la Recherche
Intracardiac electrical imaging using the 12-lead ECG: a machine learning approach using synthetic data
M Landajuela, R Anirudh, J Loscazo, R Blake
2022 Computing in Cardiology (CinC) 498, 1-4, 2022
Zorunlu olanlar: US Department of Energy
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