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Seunghyeon Cho

2 accepted papers

2024

Operator-Learning-Inspired Modeling of Neural Ordinary Differential Equations

AAAI 2024technical

Neural ordinary differential equations (NODEs), one of the most influential works of the differential equation-based deep learning, are to continuously generalize residual networks and opened a new field. They are currently utilized for various downstream tasks, e.g., image classification, time seri…

Cited by 3SourcePDFScholar
2022

GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks

NeurIPS 2022accept

Time series synthesis is an important research topic in the field of deep learning, which can be used for data augmentation. Time series data types can be broadly classified into regular or irregular. However, there are no existing generative models that show good performance for both types without…

Cited by 68SourcePDFScholar