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cs.LG2021
Sig-Wasserstein GANs for Time Series Generation
Hao Ni, Lukasz Szpruch, Marc Sabate-Vidales +3
Synthetic data is an emerging technology that can significantly accelerate the development and deployment of AI machine learning pipelines. In this work, we develop high-fidelity t…
cs.LG2020
Conditional Sig-Wasserstein GANs for Time Series Generation
Shujian Liao, Hao Ni, Lukasz Szpruch +3
Generative adversarial networks (GANs) have been extremely successful in generating samples, from seemingly high dimensional probability measures. However, these methods struggle t…