Name
Abstract | ||
|---|---|---|
In this paper, we describe a research agenda for deriving design principles directly from data. We argue that it is time to go beyond manually curated and applied visualization design guidelines. We propose learning models of visualization design from data collected using graphical perception studies and build tools powered by the learned models. To achieve this vision, we need to 1) develop scalable methods for collecting training data, 2) collect different forms of training data, 3) advance interpretability of machine learning models, and 4) develop adaptive models that evolve as more data becomes available. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Human-Computer Interaction | Training set,Design elements and principles,Interpretability,Computer science,Visualization,Human–computer interaction,Heuristics,Learning models,Perception,Scalability |
DocType | Volume | Citations |
Journal | abs/1807.06641 | 1 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bahador Saket | 1 | 140 | 11.70 |
| Dominik Moritz | 2 | 372 | 16.60 |
| Halden Lin | 3 | 1 | 0.34 |
| Victor Dibia | 4 | 31 | 4.39 |
| Çagatay Demiralp | 5 | 235 | 29.10 |
| Jeffrey Heer | 6 | 5322 | 349.19 |