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cs.LG2024
Masked Language Modeling Becomes Conditional Density Estimation for Tabular Data Synthesis
Seunghwan An, Gyeongdong Woo, Jaesung Lim +3
In this paper, our goal is to generate synthetic data for heterogeneous (mixed-type) tabular datasets with high machine learning utility (MLu). Since the MLu performance depends on…
cs.LG2024★ 9 cited
Improving SMOTE via Fusing Conditional VAE for Data-adaptive Noise Filtering
Sungchul Hong, Seunghwan An, Jong-June Jeon
Recent advances in a generative neural network model extend the development of data augmentation methods. However, the augmentation methods based on the modern generative models fa…