Results 21 to 30 of about 46,859 (313)
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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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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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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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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Hyperparameter Optimization with Differentiable Metafeatures
CoRR, 2021 Metafeatures, or dataset characteristics, have been shown to improve the performance of hyperparameter optimization (HPO). Conventionally, metafeatures are precomputed and used to measure the similarity between datasets, leading to a better initialization of HPO models.
Hadi S. Jomaa +2 more
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Bayesian Optimized Echo State Network Applied to Short-Term Load Forecasting
Energies, 2020 Load forecasting impacts directly financial returns and information in electrical systems planning. A promising approach to load forecasting is the Echo State Network (ESN), a recurrent neural network for the processing of temporal dependencies.
Gabriel Trierweiler Ribeiro +4 more
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A Novel Graph Convolutional Gated Recurrent Unit Framework for Network-Based Traffic Prediction
IEEE Access, 2023 A Smart City is characterized mainly as an efficient, technologically advanced, green, and socially informed city. An intelligent transportation system (ITS) is a subset area of smart cities that enhances the safety and mobility of road vehicles.
Basharat Hussain +4 more
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Is one hyperparameter optimizer enough? [PDF]
Proceedings of the 4th ACM SIGSOFT International Workshop on Software Analytics, 2018 Hyperparameter tuning is the black art of automatically finding a good combination of control parameters for a data miner. While widely applied in empirical Software Engineering, there has not been much discussion on which hyperparameter tuner is best for software analytics.
Huy Tu, Vivek Nair
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