Results 21 to 30 of about 127,719 (261)

Optimization of hyperparameters for SMS reconstruction [PDF]

Magnetic Resonance Imaging, 2020
Simultaneous multi-slice (SMS) imaging accelerates MRI data acquisition by exciting multiple image slices simultaneously. Overlapping slices are then separated using a mathematical model. Several parameters used in SMS reconstruction impact the quality of final images. Therefore, finding an optimal set of reconstruction parameters is critical to ensure
Muftuler, L. Tugan, Arpinar, Volkan Emre, Koch, Kevin, Bhave, Sampada, Yang, Baolian, Kaushik, Sivaram, Banerjee, Suchandrima, Nencka, Andrew   +7 more
openaire   +3 more sources

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.
openaire   +4 more sources

A Novel Graph Convolutional Gated Recurrent Unit Framework for Network-Based Traffic Prediction

IEEE Access, 2023
A Smart City is characterized mainly as an efficient, technologically advanced, green, and socially informed city. An intelligent transportation system (ITS) is a subset area of smart cities that enhances the safety and mobility of road vehicles.
Basharat Hussain, Muhammad Khalil Afzal, Sheraz Anjum, Imran Rao, Byung-Seo Kim   +4 more
doaj   +1 more source

Impact of Hyperparameter Optimization on Cross-Version Defect Prediction: An Empirical Study [PDF]

Jisuanji kexue yu tansuo, 2023
In the field of machine learning, hyperparameters are one of the key factors that affect prediction performance. Previous studies have shown that optimizing hyperparameters can improve the performance of inner-version defect prediction and cross-project ...
HAN Hui, YU Qiao, ZHU Yi
doaj   +1 more source

Symbolic Explanations for Hyperparameter Optimization

International Conference on AutoML, 2023
Hyperparameter optimization (HPO) methods can determine well-performing hyperparameter configurations efficiently but often lack insights and transparency. We propose to apply symbolic regression to meta-data collected with Bayesian optimization (BO) during HPO.
Segel, Sarah, Graf, Helena, Tornede, Alexander, Bischl, Bernd, Lindauer, Marius   +4 more
openaire   +3 more sources

Fault Diagnosis of Motor Bearings Based on a Convolutional Long Short-Term Memory Network of Bayesian Optimization

IEEE Access, 2021
As the main driving equipment of modern industrial production activities, if a motor fails, it causes serious consequences. Bearings are the component with the highest motor failure frequency.
Zhen Li, Yang Wang, Jianeng Ma
doaj   +1 more source

Frugal Optimization for Cost-related Hyperparameters

, 2020
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.
Huang, Silu, Wang, Chi, Wu, Qingyun
core   +2 more sources

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, Angky Edbert Valencio, Prasetyo Simeon Yuda   +2 more
doaj   +1 more source

Bayesian off-line detection of multiple change-points corrupted by multiplicative noise : application to SAR image edge detection [PDF]

, 2003
This paper addresses the problem of Bayesian off-line change-point detection in synthetic aperture radar images. The minimum mean square error and maximum a posteriori estimators of the changepoint positions are studied.
Andre-Obrecht, Basseville, Bernardo, Bovik, Bretthorst, Carlin, Chib, Chib, Cinlar, Curlander, Delyon, Djuric, Fjφrtoft, Frost, Geman, Geyer, Gilks, Godsill, Green, Gustafsson, Kelly, Lavielle, Lavielle, McCulloch, McCulloch, Oliver, Phillips, Robert, Robert, Ruanaidh, Sadler, Stephens, Stephens, Swami, Tierney, Touzi, Van Trees, Vincent, Willsky, Winkler   +39 more
core   +3 more sources

Promoting Fairness through Hyperparameter Optimization [PDF]

2021 IEEE International Conference on Data Mining (ICDM), 2021
Considerable research effort has been guided towards algorithmic fairness but real-world adoption of bias reduction techniques is still scarce. Existing methods are either metric- or model-specific, require access to sensitive attributes at inference time, or carry high development or deployment costs.
Cruz, André F., Saleiro, Pedro, Belém, Catarina, Soares, Carlos, Bizarro, Pedro   +4 more
openaire   +2 more sources