How do Recent Machine Learning Advances Impact the Data Visualization Research Agenda?

Timo Ropinski Ulm University Daniel Archambault University of Swansea Min Chen Oxford University Ross Maciejewski Arizona State University Klaus Mueller Stony Brook University Alexandru Telea Utrecht University Martin Wattenberg Google

2017

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Abstract

Nowadays, machine learning approaches have revolutionized many domains, by enabling machines to solve problems which could be- fore solely be solved when involving humans. As this pushes the human out of the loop, the human-in-the-loop paradigm, which is one of the main pillars of data visualization research, might be endangered. Thus, we would like to investigate, which old visualization challenges are rendered obsolete, and which new visualization challenges arise from the recent advances in machine learning. Along these lines, we will - among other aspects - investigate the role of visualization when training networks, but also in how to make machine-made decisions more transparent to humans.

Bibtex

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@misc{ropinski17mlpanel,
	title={How do Recent Machine Learning Advances Impact the Data Visualization Research Agenda?},
	author={Ropinski, Timo and Archambault, Daniel and Chen, Min and Maciejewski, Ross and Mueller, Klaus and Telea, Alexandru and Wattenberg, Martin},
	year={2017},
	howPublished={IEEE VIS Panel}
}

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