Results 11 to 20 of about 5,001,817 (246)
Frontiers in Medicine, 2023 Renal diseases are common health problems that affect millions of people around the world. Among these diseases, kidney stones, which affect anywhere from 1 to 15% of the global population and thus; considered one of the leading causes of chronic kidney ...
M. Badawy +5 more
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Hyperparameter Optimization for AST Differencing
IEEE Transactions on Software Engineering, 2023 Computing the differences between two versions of the same program is an essential task for software development and software evolution research. AST differencing is the most advanced way of doing so, and an active research area. Yet, AST differencing algorithms rely on configuration parameters that may have a strong impact on their effectiveness.
Matias Martinez +2 more
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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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Artificial Neural Network Hyperparameters Optimization: A Survey
Int. J. Online Biomed. Eng., 2022 Machine-learning (ML) methods often utilized in applications like computer vision, recommendation systems, natural language processing (NLP), as well as user behavior analytics.
Zahraa Saddi Kadhim +2 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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IEEE Transactions on Neural Networks and Learning Systems, 2021 The performance of a convolutional neural network (CNN) heavily depends on its hyperparameters. However, finding a suitable hyperparameters configuration is difficult, challenging, and computationally expensive due to three issues, which are 1) the mixed-
Jian-Yu Li +4 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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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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