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A sparrow search algorithm-optimized LSTM framework with EMD denoising for rolling element bearing remaining useful life prediction. [PDF]
Sci RepLi Q, Zhang B, Fang X.
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Adaptive demand forecasting framework with weighted ensemble of regression and machine learning models along life cycle variability. [PDF]
Sci RepHammam IM, El-Kharbotly AK, Sadek YM.
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Reproducible Hyperparameter Optimization
Journal of Computational and Graphical Statistics, 2021A key issue in machine learning research is the lack of reproducibility. We illustrate what role hyperparameter search plays in this problem and how regular hyperparameter search methods can lead t...
Lars Hertel +2 more
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Hyperparameter Optimization Machines
2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2016Algorithm selection and hyperparameter tuning are omnipresent problems for researchers and practitioners. Hence, it is not surprising that the efforts in automatizing this process using various meta-learning approaches have been increased. Sequential model-based optimization (SMBO) is ne of the most popular frameworks for finding optimal hyperparameter
Martin Wistuba +2 more
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Hyperparameter estimation in forecast models
Computational Statistics & Data Analysis, 1999zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lopes, Hedibert Freitas +2 more
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No More Pesky Hyperparameters: Offline Hyperparameter Tuning For Reinforcement Learning
2021The 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.
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Kriging Hyperparameter Tuning Strategies
AIAA Journal, 2008Response surfaces have been extensively used as a method of building effective surrogate models of high-fidelity computational simulations. Of the numerous types of response surface models, kriging is perhaps one of the most effective, due to its ability to model complicated responses through interpolation or regression of known data while providing an
Toal, David J.J. +2 more
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Learning hyperparameter optimization initializations
2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2015Hyperparameter optimization is often done manually or by using a grid search. However, recent research has shown that automatic optimization techniques are able to accelerate this optimization process and find hyperparameter configurations that lead to better models.
Martin Wistuba +2 more
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