May 14, 2024, 4:44 a.m. | Ilan Naiman, N. Benjamin Erichson, Pu Ren, Michael W. Mahoney, Omri Azencot

cs.LG updates on

arXiv:2310.02619v2 Announce Type: replace
Abstract: Generating realistic time series data is important for many engineering and scientific applications. Existing work tackles this problem using generative adversarial networks (GANs). However, GANs are unstable during training, and they can suffer from mode collapse. While variational autoencoders (VAEs) are known to be more robust to the these issues, they are (surprisingly) less considered for time series generation. In this work, we introduce Koopman VAE (KoVAE), a new generative framework that is based on …

abstract adversarial applications arxiv autoencoders cs.lg data engineering gans generative generative adversarial networks generative modeling however modeling networks replace scientific series time series training type variational autoencoders via while work

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