2 papers
cs.LG2024
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…
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…