4 citations · 9 across the 6 of their papers we have counts for
6 papers
Offsetting Perceptual Bias in Visual Clustering: The Role of Point Size Adjustment in Variable Display Sizes
Taehyun Yang, Hyeon Jeon, Jinwook Seo
Scatterplots are frequently shared across different displays in collaborative and communicative visual analytics. However, variations in displays diversify scatterplot sizes. Such…
Extracting Human Attention through Crowdsourced Patch Labeling
Minsuk Chang, Seokhyeon Park, Hyeon Jeon +3
In image classification, a significant problem arises from bias in the datasets. When it contains only specific types of images, the classifier begins to rely on shortcuts - simpli…
CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models
Juhye Ha, Hyeon Jeon, DaEun Han +2
Large language models (LLMs) have facilitated significant strides in generating conversational agents, enabling seamless, contextually relevant dialogues across diverse topics. How…
CLAMS: A Cluster Ambiguity Measure for Estimating Perceptual Variability in Visual Clustering
Hyeon Jeon, Ghulam Jilani Quadri, Hyunwook Lee +3
Visual clustering is a common perceptual task in scatterplots that supports diverse analytics tasks (e.g., cluster identification). However, even with the same scatterplot, the way…
ZADU: A Python Library for Evaluating the Reliability of Dimensionality Reduction Embeddings
Hyeon Jeon, Aeri Cho, Jinhwa Jang +5
Dimensionality reduction (DR) techniques inherently distort the original structure of input high-dimensional data, producing imperfect low-dimensional embeddings. Diverse distortio…
Classes are not Clusters: Improving Label-based Evaluation of Dimensionality Reduction
Hyeon Jeon, Yun-Hsin Kuo, Michaël Aupetit +2
A common way to evaluate the reliability of dimensionality reduction (DR) embeddings is to quantify how well labeled classes form compact, mutually separated clusters in the embedd…