Results 11 to 20 of about 38,367 (259)
Optimizing microservices with hyperparameter optimization
2021 17th International Conference on Mobility, Sensing and Networking (MSN), 2021 In the last few years, the cloudification of applications requires new concepts and techniques to fully reap the benefits of the new computing paradigm. Among them, the microservices architectural style, which is inspired by service-oriented architectures, has gained attention from both industry and academia.
Hai Dinh-Tuan +2 more
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IEEE Access, 2022 On-the-fly learning is unavoidable for applications that demand instantaneous deep neural network (DNN) training or where transferring data to the central system for training is costly.
Anjir Ahmed Chowdhury +3 more
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PyHopper -- Hyperparameter optimization
CoRR, 2022 Hyperparameter tuning is a fundamental aspect of machine learning research. Setting up the infrastructure for systematic optimization of hyperparameters can take a significant amount of time. Here, we present PyHopper, a black-box optimization platform designed to streamline the hyperparameter tuning workflow of machine learning researchers. PyHopper's
Mathias Lechner +4 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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Convolutional neural network hyperparameter optimization applied to land cover classification
Радіоелектронні і комп'ютерні системи, 2022 In recent times, machine learning algorithms have shown great performance in solving problems in different fields of study, including the analysis of remote sensing images, computer vision, natural language processing, medical issues, etc.
Vladyslav Yaloveha +2 more
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Impact of Hyperparameter Optimization on Cross-Version Defect Prediction: An Empirical Study [PDF]
Jisuanji kexue yu tansuo, 2023 In the field of machine learning, hyperparameters are one of the key factors that affect prediction performance. Previous studies have shown that optimizing hyperparameters can improve the performance of inner-version defect prediction and cross-project ...
HAN Hui, YU Qiao, ZHU Yi
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Understanding Bitcoin Price Prediction Trends under Various Hyperparameter Configurations
Computers, 2022 Since bitcoin has gained recognition as a valuable asset, researchers have begun to use machine learning to predict bitcoin price. However, because of the impractical cost of hyperparameter optimization, it is greatly challenging to make accurate ...
Jun-Ho Kim, Hanul Sung
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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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Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis
Informatics, 2021 Machine learning models are used today to solve problems within a broad span of disciplines. If the proper hyperparameter tuning of a machine learning classifier is performed, significantly higher accuracy can be obtained.
Enas Elgeldawi +3 more
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