9 citations · 16 across the 9 of their papers we have counts for
9 papers
Estimating Subgraph Importance with Structural Prior Domain Knowledge
Changhyun Kim, Seunghwan An, Jong-June Jeon
We propose a subgraph importance estimation method for pretrained Graph Neural Networks (GNNs) on graph-level tasks, formulated as a linear Group Lasso regression problem in the em…
QATMA: Quantization-Aware Training with Multimodal Alignment for Open-Vocabulary Object Detection
Jinyeong Park, Donghwa Kang, Seunghwan An +4
Quantizing open-vocabulary object detection (OVOD) models reduces their memory and computational costs, but extremely low-bit quantization severely degrades both cross-modal (regio…
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…
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…
Balanced Marginal and Joint Distributional Learning via Mixture Cramer-Wold Distance
Seunghwan An, Sungchul Hong, Jong-June Jeon
In the process of training a generative model, it becomes essential to measure the discrepancy between two high-dimensional probability distributions: the generative distribution a…
Joint Distributional Learning via Cramer-Wold Distance
Seunghwan An, Jong-June Jeon
The assumption of conditional independence among observed variables, primarily used in the Variational Autoencoder (VAE) decoder modeling, has limitations when dealing with high-di…
Causally Disentangled Generative Variational AutoEncoder
Seunghwan An, Kyungwoo Song, Jong-June Jeon
We present a new supervised learning technique for the Variational AutoEncoder (VAE) that allows it to learn a causally disentangled representation and generate causally disentangl…
Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation
Seunghwan An, Jong-June Jeon
The Gaussianity assumption has been consistently criticized as a main limitation of the Variational Autoencoder (VAE) despite its efficiency in computational modeling. In this pape…
EXoN: EXplainable encoder Network
SeungHwan An, Hosik Choi, Jong-June Jeon
We propose a new semi-supervised learning method of Variational AutoEncoder (VAE) which yields a customized and explainable latent space by EXplainable encoder Network (EXoN). Cust…