Results 21 to 30 of about 129,562 (262)

Bayesian Optimized Echo State Network Applied to Short-Term Load Forecasting

Energies, 2020
Load forecasting impacts directly financial returns and information in electrical systems planning. A promising approach to load forecasting is the Echo State Network (ESN), a recurrent neural network for the processing of temporal dependencies.
Gabriel Trierweiler Ribeiro, João Guilherme Sauer, Naylene Fraccanabbia, Viviana Cocco Mariani, Leandro dos Santos Coelho   +4 more
doaj   +1 more source

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

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

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

A Comparison of AutoML Hyperparameter Optimization Tools For Tabular Data

Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2023
The performance of machine learning (ML) methods for classification and regression tasks applied to tabular datasets is sensitive to hyperparameters values.
Prativa Pokhrel, Alina Lazar
doaj   +1 more source