Search Results - subject:"Generative Adversarial Networks"

Sig-Wasserstein GANs for conditional time series generation

Liao, Shujian; Ni, Hao; Sabate-Vidales, Marc; Szpruch, … - In: Mathematical finance : an international journal of … 34 (2024) 2, pp. 622-670

Persistent link: https://www.econbiz.de/10014514799

Effective machine learning techniques for dealing with poor credit data

Dumisani Selby Nkambule; Twala, Bhekisipho; Pretorious, … - In: Risks : open access journal 12 (2024) 11, pp. 1-19

and MCC metrics. Then, the harnessing of generative adversarial networks (GANs) simulation to enhance the robustness of …

Persistent link: https://www.econbiz.de/10015135786

Forecasting VaR and ES by using deep quantile regression, GANs-based scenario generation, and heterogeneous market hypothesis

Wang, Jianzhou; Wang, Shuai; Lv, Mengzheng; Jiang, He - In: Financial innovation : FIN 10 (2024), pp. 1-35

and employs generative adversarial networks to generate future tail risk scenarios. In addition to the typical properties …

Persistent link: https://www.econbiz.de/10014530222

Where's Waldo? : a framework for quantifying the privacy-utility trade-off in marketing applications

Ponte, Gilian R.; Wieringa, Jaap E.; Boot, Tom; … - In: International journal of research in marketing : IJRM ; … 41 (2024) 3, pp. 529-546

Persistent link: https://www.econbiz.de/10015057560

Anomalous node detection in attributed social networks using dual variational autoencoder with generative adversarial networks

Khan, Wasim; Abidin, Shafiqul; Arif, Mohammad; Ishrat, … - In: Data science and management : DSM 7 (2024) 2, pp. 89-98

Many types of real-world information systems, including social media and e-commerce platforms, can be modelled by means of attribute-rich, connected networks. The goal of anomaly detection in artificial intelligence is to identify illustrations that deviate significantly from the main...

Persistent link: https://www.econbiz.de/10015063137

A cluster-based human resources analytics for predicting employee turnover using optimized Artificial Neural Networks and data augmentation

Shafie, Mohammad Reza; Khosravi, Hamed; Farhadpour, Sarah; … - In: Decision analytics journal 11 (2024), pp. 1-17

Adversarial Networks (CTGAN) is performed on clusters with imbalanced data. Following this, the optimized ANN models are applied … implemented to improve the efficiency and effectiveness of retention policies. Data augmentation using Conditional Generative …

Persistent link: https://www.econbiz.de/10015101907

Product aesthetic design : a machine learning augmentation

Burnap, Alex; Hauser, John R.; Timoshenko, Artem - In: Marketing science 42 (2023) 6, pp. 1029-1056

Persistent link: https://www.econbiz.de/10014436737

An adversarial approach to structural estimation

Kaji, Tetsuya; Manresa, Elena; Pouliot, Guillaume - In: Econometrica : journal of the Econometric Society, an … 91 (2023) 6, pp. 2041-2063

Persistent link: https://www.econbiz.de/10014438258

Scenario generation for market risk models using generative neural networks

Flaig, Thekla Solveig; Junike, Gero - In: Risks : open access journal 10 (2022) 11, pp. 1-28

In this research study, we show how existing approaches of using generative adversarial networks (GANs) as economic …

Persistent link: https://www.econbiz.de/10013556779

Product Aesthetic Design : A Machine Learning Augmentation

Burnap, Alex; Hauser, John R.; Timoshenko, Artem - 2022

variational autoencoder (VAE), adversarial components from generative adversarial networks (GAN), and a supervised learning …

Persistent link: https://www.econbiz.de/10014242270