Results 21 to 30 of about 1,858,266 (298)
地质科技通报, 2022 In machine learning-based landslide susceptibility assessment, there are some differences in the evaluation results obtained by using different hyperparameters.
Can Yang +4 more
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Buildings, 2023 Building energy optimization (BEO) is a promising technique to achieve energy efficient designs. The efficacy of optimization algorithms is imperative for the BEO technique and is significantly dependent on the algorithm hyperparameters.
Binghui Si, Feng Liu, Yanxia Li
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Data-driven approaches for identifying hyperparameters in multi-step retrosynthesis [PDF]
, 2023 Multi-step retrosynthesis problem can be solved by a search algorithm, such as Monte Carlo tree search (MCTS). The performance of multistep retrosynthesis, as measured by a trade-off in search time and route solvability, therefore depends on the ...
Lewis, Mervin +3 more
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Hyperparameter Optimization of CNN for Map Building
Современные информационные технологии и IT-образование, 2020 This article describes an approach for solving the task of finding hyperparameters of an artificial neural network, which is used for making a 2D land map.
Alexandra Akinina, Mikhail Nikiforov
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Tuning of Bayesian optimization for materials synthesis: simulation of the one-dimensional case
Science and Technology of Advanced Materials: Methods, 2022 Materials exploration requires the optimization of a multidimensional space including the chemical composition and synthesis parameters such as temperature and pressure.
Ryo Nakayama +8 more
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Replication Data for: eSLP optimization algorithm
, 2023 m-files implementing the eSLP optimization algorithm for the three simulations (3.1. - 3.3.) described in the associated paper. File README.txt contains analytical directions regarding requirements, how to verify the results and videos generated by the ...
Optimization, eSLP
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Hyperparameters values used during model optimization.
, 2021 Hyperparameters values used during model optimization.
Ethel Dominique Viray (11564842) +6 more
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Tuning hyperparameters of doublet‐detection methods for single‐cell RNA sequencing data
Quantitative Biology, 2023 Doublet is a major confounder in single‐cell RNA sequencing data analysis. Computational doublet‐detection methods aim to remove doublets from scRNA‐seq data. The performance of those methods relies on the appropriate setting of their hyperparameters. In
Nan Miles Xi, Angelos Vasilopoulos
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Symbolic Explanations for Hyperparameter Optimization
International Conference on AutoML, 2023 Hyperparameter optimization (HPO) methods can determine well-performing hyperparameter configurations efficiently but often lack insights and transparency. We propose to apply symbolic regression to meta-data collected with Bayesian optimization (BO) during HPO.
Segel, Sarah +4 more
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Use of Static Surrogates in Hyperparameter Optimization [PDF]
Operations Research Forum, 2022 http://www.optimization-online.org/DB_HTML/2021/03/8296 ...
Dounia Lakhmiri, Sébastien Le Digabel
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