11 citations · 20 across the 7 of their papers we have counts for
7 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…
Does a Large Language Model Really Speak in Human-Like Language?
Mose Park, Yunjin Choi, Jong-June Jeon
Large Language Models (LLMs) have recently emerged, attracting considerable attention due to their ability to generate highly natural, human-like text. This study compares the late…
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…
Geodesic Multi-Modal Mixup for Robust Fine-Tuning
Changdae Oh, Junhyuk So, Hoyoon Byun +4
Pre-trained multi-modal models, such as CLIP, provide transferable embeddings and show promising results in diverse applications. However, the analysis of learned multi-modal embed…