Results 51 to 60 of about 6,929,542 (320)
Asynchronous Decentralized Bayesian Optimization for Large Scale Hyperparameter Optimization
, 2023 International audienceBayesian optimization (BO) is a promising approach for hyperparameter optimization of deep neural networks (DNNs), where each model training can take minutes to hours.
Guyon, Isabelle +3 more
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Hyperparameter Tuning on Classification Algorithm with Grid Search
Sistemasi: Jurnal Sistem Informasi, 2022 Currently, machine learning algorithms continue to be developed to perform optimization with various methods to produce the best-performing model. In Supervised learning or classification, most of the algorithms have hyperparameters.
Wahyu Nugraha, Agung Sasongko
doaj +1 more source
Advanced hyperparameter optimization of deep learning models for wind power prediction
Renewable Energy, 2023 The uncertainty of wind power as the main obstacle of its integration into the power grid can be addressed by an accurate and efficient wind power forecast.
Shahram Hanifi +2 more
semanticscholar +1 more source
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
openaire +4 more sources
Improving stroke diagnosis accuracy using hyperparameter optimized deep learning
IJAIN (International Journal of Advances in Intelligent Informatics), 2019 Stroke may cause death for anyone, including youngsters. One of the early stroke detection techniques is a Computerized Tomography (CT) scan. This research aimed to optimize hyperparameter in Deep Learning, Random Search and Bayesian Optimization for ...
Tessy Badriyah +3 more
doaj +1 more source
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
openaire +3 more sources
Hyperparameter Optimization with Neural Network Pruning [PDF]
, 2022 Since the deep learning model is highly dependent on hyperparameters, hyperparameter optimization is essential in developing deep learning model-based applications, even if it takes a long time.
Yim, Junho, Lee, Kangil
core
Exploratory Landscape Validation for Bayesian Optimization Algorithms
MathematicsBayesian optimization algorithms are widely used for solving problems with a high computational complexity in terms of objective function evaluation. The efficiency of Bayesian optimization is strongly dependent on the quality of the surrogate models of ...
Taleh Agasiev, Anatoly Karpenko
doaj +1 more source
Hyperparameter optimization in recommender systems : Bayesian optimization vs. Nelder-Mead
, 2021 LAUREA MAGISTRALEIn recent years, especially with the developments in machine learning and data mining, recommender systems have gained increased popularity.
Bujari, Diedon
core
Algorithms, 2023 Heart disease is a significant global health issue, contributing to high morbidity and mortality rates. Early and accurate heart disease prediction is crucial for effectively preventing and managing the condition. However, this remains a challenging task
Daniyal Asif +3 more
semanticscholar +1 more source