Results 51 to 60 of about 47,583 (209)
Better and faster hyperparameter optimization with Dask [PDF]
Proceedings of the Python in Science Conference, 2019 Slides about a new hyperparameter optimization algorithm in ...
Scott Sievert +2 more
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Optimizing Deep Learning Models with Improved BWO for TEC Prediction
BiomimeticsThe prediction of total ionospheric electron content (TEC) is of great significance for space weather monitoring and wireless communication. Recently, deep learning models have become increasingly popular in TEC prediction.
Yi Chen +6 more
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Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimizationb
Journal of Electronic Science and Technology, 2019 Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models.
Jia Wu +5 more
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Metalearning for Hyperparameter Optimization [PDF]
, 2022 SummaryThis chapter describes various approaches for the hyperparameter optimization (HPO) and combined algorithm selection and hyperparameter optimization problems (CASH). It starts by presenting some basic hyperparameter optimization methods, including grid search, random search, racing strategies, successive halving and hyperband. Next, it discusses
Brazdil, Pavel +3 more
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A Hybrid Sparrow Search Algorithm of the Hyperparameter Optimization in Deep Learning
Mathematics, 2022 Deep learning has been widely used in different fields such as computer vision and speech processing. The performance of deep learning algorithms is greatly affected by their hyperparameters.
Yanyan Fan +5 more
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Neighbor Regularized Bayesian Optimization for Hyperparameter Optimization
CoRR, 2022 Bayesian Optimization (BO) is a common solution to search optimal hyperparameters based on sample observations of a machine learning model. Existing BO algorithms could converge slowly even collapse when the potential observation noise misdirects the optimization.
Lei Cui +4 more
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IEEE Access, 2019 Heart failure is considered one of the leading cause of death around the world. The diagnosis of heart failure is a challenging task especially in under-developed and developing countries where there is a paucity of human experts and equipments.
Ashir Javeed +5 more
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Optimization of Annealed Importance Sampling Hyperparameters
, 2023 AbstractAnnealed Importance Sampling (AIS) is a popular algorithm used to estimates the intractable marginal likelihood of deep generative models. Although AIS is guaranteed to provide unbiased estimate for any set of hyperparameters, the common implementations rely on simple heuristics such as the geometric average bridging distributions between ...
Shirin Goshtasbpour +1 more
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An Effective Hyperparameters Optimization Algorithm for Metro Passenger Flow Prediction
, 2021 An accurate neural network model must rely on suitable model architecture and an appropriate model training process. The hyperparameters optimization problem aims to search for the best neural network architecture in a large solution space and maximize ...
Fang, Zhi-Yan
core
Design of adaptive soft sensor based on Bayesian optimization
Case Studies in Chemical and Environmental Engineering, 2022 When adaptive soft sensors are introduced to industrial plants, an appropriate combination of the type of adaptation mechanism, hyperparameters of the mechanism, regression model, and hyperparameters of the model must be selected for predictive soft ...
Shuto Yamakage, Hiromasa Kaneko
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