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High Per Parameter: A Large-Scale Study of Hyperparameter Tuning for Machine Learning Algorithms
Algorithms, 2022 Hyperparameters in machine learning (ML) have received a fair amount of attention, and hyperparameter tuning has come to be regarded as an important step in the ML pipeline. However, just how useful is said tuning?
Moshe Sipper
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To tune or not to tune? An Approach for Recommending Important Hyperparameters
CoRR, 2021 Presented on The Fifth International Workshop on Automation in Machine Learning, A workshop to be held in conjunction with the KDD 2021 ...
Mohamadjavad Bahmani +3 more
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Journal of Ocean Engineering and Science, 2022 An accurate prediction of ship fuel consumption is critical for speed, trim, and voyage optimisation etc. While previous studies have focused on predicting ship fuel consumption with respect to a variety of factors, research on the impact of ...
Tianrui Zhou +3 more
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No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL
CoRR, 2022 The performance of reinforcement learning (RL) agents is sensitive to the choice of hyperparameters. In real-world settings like robotics or industrial control systems, however, testing different hyperparameter configurations directly on the environment can be financially prohibitive, dangerous, or time consuming.
Han Wang 0066 +9 more
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Hyperparameter self-tuning for data streams [PDF]
Information Fusion, 2021 Abstract The number of Internet of Things devices generating data streams is expected to grow exponentially with the support of emergent technologies such as 5G networks. Therefore, the online processing of these data streams requires the design and development of suitable machine learning algorithms, able to learn online, as data is generated.
Bruno Veloso +3 more
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Performance Evaluation of Regression Models for the Prediction of the COVID-19 Reproduction Rate
Frontiers in Public Health, 2021 This paper aims to evaluate the performance of multiple non-linear regression techniques, such as support-vector regression (SVR), k-nearest neighbor (KNN), Random Forest Regressor, Gradient Boosting, and XGBOOST for COVID-19 reproduction rate prediction
Jayakumar Kaliappan +5 more
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Rethinking the Hyperparameters for Fine-tuning
CoRR, 2020 Published as a conference paper at ICLR ...
Hao Li +6 more
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Diagnostics, 2022 Numerous research have demonstrated that Convolutional Neural Network (CNN) models are capable of classifying visual field (VF) defects with great accuracy.
Masyitah Abu +6 more
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Hyperparameters and tuning strategies for random forest [PDF]
WIREs Data Mining and Knowledge Discovery, 2019 The random forest (RF) algorithm has several hyperparameters that have to be set by the user, for example, the number of observations drawn randomly for each tree and whether they are drawn with or without replacement, the number of variables drawn randomly for each split, the splitting rule, the minimum number of samples that a node must contain, and ...
Philipp Probst +2 more
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Optuna vs Code Llama: Are LLMs a New Paradigm for Hyperparameter Tuning? [PDF]
2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)Optimal hyperparameter selection is critical for maximizing the performance of neural networks in computer vision, particularly as architectures become more complex.
Roman Kochnev +4 more
semanticscholar +1 more source