Conditional Wasserstein GAN-based oversampling of tabular data for imbalanced learning

Author:

Engelmann Justin,Lessmann Stefan

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference37 articles.

1. Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein GAN. ArXiv pre-print, arXiv:1701.07875.

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3. Synthesizing electronic health records using improved generative adversarial networks;Baowaly;Journal of the American Medical Informatics Association,2019

4. Bellemare, M.G., Danihelka, I., Dabney, W., Mohamed, S., Lakshminarayanan, B., Hoyer, S., & Munos, R. (2017). The Cramer Distance as a Solution to Biased Wasserstein Gradients. ArXiv pre-print, arXiv:1705.10743.

5. Bengio, Y., Léonard, N., & Courville, A. (2013). Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation. ArXiv pre-print, arXiv:1308.3432.

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