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  • Search: subject:"conditional generative adversarial networks"
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Subject
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Arbeitsmobilität 1 Artificial Neural Networks 1 Artificial intelligence 1 Cluster analysis 1 Clusteranalyse 1 Conditional Generative Adversarial Networks 1 Employee turnover 1 Forecasting model 1 Human Resource Management 1 Human resources 1 Human resources analytics 1 Humanressourcen 1 Künstliche Intelligenz 1 Labour mobility 1 Machine learning 1 Neural networks 1 Neuronale Netze 1 Personalmanagement 1 Prognoseverfahren 1 Theorie 1 Theory 1 Time series analysis 1 Wasserstein generative adversarial networks 1 Zeitreihenanalyse 1 conditional generative adversarial networks 1 generative adversarial networks 1 rough path theory 1 time series modeling 1
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Online availability
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CC license 1 Free 1 Undetermined 1
Type of publication
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Article 2
Type of publication (narrower categories)
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Article in journal 2 Aufsatz in Zeitschrift 2
Language
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English 2
Author
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Ahmed, Imtiaz 1 Das, Srinjoy 1 Farhadpour, Sarah 1 Khosravi, Hamed 1 Liao, Shujian 1 Ni, Hao 1 Sabate-Vidales, Marc 1 Shafie, Mohammad Reza 1 Szpruch, Łukasz 1 Wiese, Magnus 1 Xiao, Baoren 1
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Decision analytics journal 1 Mathematical finance : an international journal of mathematics, statistics and financial economics 1
Source
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ECONIS (ZBW) 2
Showing 1 - 2 of 2
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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
implemented to improve the efficiency and effectiveness of retention policies. Data augmentation using Conditional Generative … Adversarial Networks (CTGAN) is performed on clusters with imbalanced data. Following this, the optimized ANN models are applied …
Persistent link: https://www.econbiz.de/10015101907
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Cover Image
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
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