Results 31 to 40 of about 43,032 (264)
Improving Deep Learning-Based Recommendation Attack Detection Using Harris Hawks Optimization
Applied Sciences, 2022 Recommendation attack attempts to bias the recommendation results of collaborative recommender systems by injecting malicious ratings into the rating database. A lot of methods have been proposed for detecting such attacks.
Quanqiang Zhou +2 more
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Age estimation through facial images using Deep CNN Pretrained Model and Particle Swarm Optimization [PDF]
E3S Web of Conferences, 2023 There has been a lot of recent study on age estimates utilizing different optimization techniques, architecture models, and diverse strategies with some variations.
Muliawan Nicholas Hans +2 more
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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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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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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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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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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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Overtuning in Hyperparameter Optimization
CoRRAccepted at the Fourth Conference on Automated Machine Learning (Methods Track).
Lennart Schneider +2 more
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