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Nathaniel Haut
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2020 – today
- 2024
- [c8]Yuri Lavinas, Nathan Haut, William Punch, Wolfgang Banzhaf, Sylvain Cussat-Blanc:
Data Sampling via Active Learning in Cartesian Genetic Programming for Biomedical Data. CEC 2024: 1-8 - [c7]Yuri Lavinas
, Nathaniel Haut
Dynamically Sampling biomedical Images For Genetic Programming. GECCO Companion 2024: 515-518 - [c6]Yuri Lavinas
Adaptive Sampling of Biomedical Images with Cartesian Genetic Programming. PPSN (1) 2024: 256-272 - [i4]Illya Bakurov, Nathan Haut, Wolfgang Banzhaf:
Sharpness-Aware Minimization in Genetic Programming. CoRR abs/2405.10267 (2024) - 2023
- [c5]Nathan Haut
Active Learning Informs Symbolic Regression Model Development in Genetic Programming. GECCO Companion 2023: 587-590 - [c4]Nathan Haut, Wolfgang Banzhaf, Bill Punch, Dirk Colbry
Accelerating Image Analysis Research with Active Learning Techniques in Genetic Programming. GPTP 2023: 45-64 - [i3]Nathan Haut, Wolfgang Banzhaf, Bill Punch:
Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression. CoRR abs/2308.00672 (2023) - 2022
- [c3]Nathan Haut, Wolfgang Banzhaf, Bill Punch:
Active learning improves performance on symbolic regression tasks in StackGP. GECCO Companion 2022: 550-553 - [c2]Nathan Haut, Wolfgang Banzhaf, Bill Punch:
Correlation Versus RMSE Loss Functions in Symbolic Regression Tasks. GPTP 2022: 31-55 - [i2]Nathan Haut, Wolfgang Banzhaf, Bill Punch:
Active Learning Improves Performance on Symbolic RegressionTasks in StackGP. CoRR abs/2202.04708 (2022) - [i1]Nathan Haut, Wolfgang Banzhaf, Bill Punch:
Correlation versus RMSE Loss Functions in Symbolic Regression Tasks. CoRR abs/2205.15990 (2022) - 2021
- [c1]Mark E. Kotanchek, Nathan Haut:
Back to the Future - Revisiting OrdinalGP and Trustable Models After a Decade. GPTP 2021: 129-142
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last updated on 2024-10-07 21:20 CEST by the dblp team
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