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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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Hyperparameters Optimization for Federated Learning System : Speech Emotion Recognition Case Study
, 2023 Context: Federated Learning (FL) has emerged as a promising, massively distributed way to train a joint deep model across numerous edge devices, ensuring user data privacy by retaining it on the device.
Mohammadi, Mohammadreza, +3 more
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Improving classification accuracy of fine-tuned CNN models: Impact of hyperparameter optimization
HeliyonThe immense popularity of convolutional neural network (CNN) models has sparked a growing interest in optimizing their hyperparameters. Discovering the ideal values for hyperparameters to achieve optimal CNN training is a complex and time-consuming task,
Mikolaj Wojciuk +3 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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Automatic Termination for Hyperparameter Optimization
, 2021 Bayesian optimization (BO) is a widely popular approach for the hyperparameter optimization (HPO) in machine learning. At its core, BO iteratively evaluates promising configurations until a user-defined budget, such as wall-clock time or number of iterations, is exhausted. While the final performance after tuning heavily depends on the provided budget,
Makarova, Anastasia +7 more
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Frugal Optimization for Cost-related Hyperparameters
, 2021 The increasing demand for democratizing machine learning algorithms calls for hyperparameter optimization (HPO) solutions at low cost. Many machine learning algorithms have hyperparameters which can cause a large variation in the training cost.
Wang, Chi, Huang, Silu, Wu, Qingyun
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Immunocomputing-Based Approach for Optimizing the Topologies of LSTM Networks
IEEE Access, 2021 This paper aims to automatically design optimal LSTM topologies using the clonal selection algorithm (CSA) to solve text classification tasks such as sentiment analysis and SMS spam classification.
Ali Al Bataineh, Devinder Kaur
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Overtuning in Hyperparameter Optimization
CoRRAccepted at the Fourth Conference on Automated Machine Learning (Methods Track).
Lennart Schneider +2 more
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Theoretical Aspects in Penalty Hyperparameters Optimization
Mediterranean Journal of Mathematics, 2023 AbstractLearning processes play an important role in enhancing understanding and analyzing real phenomena. Most of these methodologies revolve around solving penalized optimization problems. A significant challenge arises in the choice of the penalty hyperparameter, which is typically user-specified or determined through Grid search approaches.
Esposito F., Selicato L., Sportelli C.
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Hyperparameters and the average optimization runtime per epoch of the best performing model.
, 2021 Hyperparameters and the average optimization runtime per epoch of the best performing model.
Ethel Dominique Viray (11564842) +6 more
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