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pro vyhledávání: '"Chang, Remco"'
Visual validation of regression models in scatterplots is a common practice for assessing model quality, yet its efficacy remains unquantified. We conducted two empirical experiments to investigate individuals' ability to visually validate linear reg
Externí odkaz:
http://arxiv.org/abs/2407.11625
Autor:
Wu, Eugene, Chang, Remco
In visualization, the process of transforming raw data into visually comprehensible representations is pivotal. While existing models like the Information Visualization Reference Model describe the data-to-visual mapping process, they often overlook
Externí odkaz:
http://arxiv.org/abs/2407.06404
Despite decision-making being a vital goal of data visualization, little work has been done to differentiate the decision-making tasks within our field. While visualization task taxonomies and typologies exist, they are often too granular for describ
Externí odkaz:
http://arxiv.org/abs/2404.08812
Autor:
Montambault, Brian, Appleby, Gabriel, Rogers, Jen, Brumar, Camelia D., Li, Mingwei, Chang, Remco
Dimensionality reduction techniques are widely used for visualizing high-dimensional data. However, support for interpreting patterns of dimension reduction results in the context of the original data space is often insufficient. Consequently, users
Externí odkaz:
http://arxiv.org/abs/2404.07386
Data integration is often performed to consolidate information from multiple disparate data sources during visual data analysis. However, integration operations are usually separate from visual analytics operations such as encode and filter in both i
Externí odkaz:
http://arxiv.org/abs/2403.04757
Autor:
Brumar, Camelia D., Appleby, Gabriel, Rogers, Jen, Matinde, Teddy, Thompson, Lara, Chang, Remco, Crisan, Anamaria
Content recommendation tasks increasingly use Graph Neural Networks, but it remains challenging for machine learning experts to assess the quality of their outputs. Visualization systems for GNNs that could support this interrogation are few. Moreove
Externí odkaz:
http://arxiv.org/abs/2310.11562
We investigate the ability of individuals to visually validate statistical models in terms of their fit to the data. While visual model estimation has been studied extensively, visual model validation remains under-investigated. It is unknown how wel
Externí odkaz:
http://arxiv.org/abs/2307.09330
Publikováno v:
IEEE Transactions on Visualization and Computer Graphics, vol. 30, no. 1, pp. 584-594, Jan. 2024
This study presents insights from interviews with nineteen Knowledge Graph (KG) practitioners who work in both enterprise and academic settings on a wide variety of use cases. Through this study, we identify critical challenges experienced by KG prac
Externí odkaz:
http://arxiv.org/abs/2304.01311
Autor:
Gleicher, Michael, Riveiro, Maria, von Landesberger, Tatiana, Deussen, Oliver, Chang, Remco, Gillman, Christina
Visualization researchers and visualization professionals seek appropriate abstractions of visualization requirements that permit considering visualization solutions independently from specific problems. Abstractions can help us design, analyze, orga
Externí odkaz:
http://arxiv.org/abs/2303.06257
The VAST Challenges have been shown to be an effective tool in visual analytics education, encouraging student learning while enforcing good visualization design and development practices. However, research has observed that students often struggle a
Externí odkaz:
http://arxiv.org/abs/2211.00567