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Scalable Meta-Bayesian Based Hyperparameters Optimization for Machine Learning
, 2022 International audienceIt is a known fact that the selection of one or more optimized algorithms and the configuration of significant hyperparameters, is among the major problems for the advanced data analytics using Machine Learning (ML) methodologies ...
Ahmad, Adeel +4 more
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IEEE Access, 2021 As the main driving equipment of modern industrial production activities, if a motor fails, it causes serious consequences. Bearings are the component with the highest motor failure frequency.
Zhen Li, Yang Wang, Jianeng Ma
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Hyperparameter Optimization [PDF]
, 2019 Recent interest in complex and computationally expensive machine learning models with many hyperparameters, such as automated machine learning (AutoML) frameworks and deep neural networks, has resulted in a resurgence of research on hyperparameter optimization (HPO). In this chapter, we give an overview of the most prominent approaches for HPO.
Feurer, Matthias, Hutter, Frank
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Materials Science Optimization Benchmark Dataset for High-dimensional, Multi-objective, Multi-fidelity Optimization of CrabNet Hyperparameters [PDF]
, 2023 Benchmarks are crucial for driving progress in scientific disciplines. To be effective, benchmarks should closely mimic real-world tasks while being computationally efficient, allowing for accessibility and repeatability. Developing surrogate models that
Jeet N., Parikh +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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Methods for Hyperparameters Optimization in Learning Approaches: an overview
, 2020 Automatic learning research focuses on the development of methods capable of extracting useful information from a given dataset. A large variety of learning methods exists, ranging from biologically inspired neural networks to statistical methods.
F. Esposito, N. Del Buono, L. Selicato
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Squirrel: A Switching Hyperparameter Optimizer
CoRR, 2020 In this short note, we describe our submission to the NeurIPS 2020 BBO challenge. Motivated by the fact that different optimizers work well on different problems, our approach switches between different optimizers. Since the team names on the competition's leaderboard were randomly generated "alliteration nicknames", consisting of an adjective and an ...
Noor H. Awad +11 more
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A Comparison of AutoML Hyperparameter Optimization Tools For Tabular Data
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2023 The performance of machine learning (ML) methods for classification and regression tasks applied to tabular datasets is sensitive to hyperparameters values.
Prativa Pokhrel, Alina Lazar
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Optimizing Deep Learning Hyperparameters Using Interpolation-Based Optimization [PDF]
Control and Optimization in Applied MathematicsHyperparameter optimization (HPO) is essential for maximizing the performance of deep learning models. Traditional approaches, such as grid search and Bayesian Optimization (BO), are widely used but can be computationally expensive.
Michael Oluwaseun Ayansiji +1 more
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Journal of Marine Science and Engineering, 2021 Accurate, reliable, and real-time prediction of ship fuel consumption is the basis and premise of the development of fuel optimization; however, ship fuel consumption data mainly come from noon reports, and many current modeling methods have been based ...
Zhihui Hu +5 more
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