“GaussBox: Prototyping Movement Interaction with Interactive Visualizations of Machine Learning,” in Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA '16), San Jose, CA, ACM, 2016, pp. 3667--3670. DOI: 10.1145/2851581.2890257. http://dl.acm.org/authorize?N03765.
We present GaussBox, a design support tool for prototyping movement interaction using machine learning. In particular, we propose novel, interactive visualizations that expose the behavior and internal values of machine learning models rather than their sole results. Such visualizations have both pedagogical and creative potentials to guide users in the exploration, experience and craft of machine learning for interaction design.Download Project Page
Acceptance Rate: 20%, “Supporting User Interaction with Machine Learning through Interactive Visualizations,” in CHI'16 Workshop on Human-Centred Machine Learning, San Jose, CA, 2016. http://www.doc.gold.ac.uk/~mas02mg/HCML2016/HCML2016_paper_20.pdf.
This paper discusses novel visualizations that expose the behavior and internal values of machine learning models rather than their sole results. Interactive visualizations have the potential to shift the perception of machine learning models from black-box processes to transparent artifacts that can be experienced and crafted. We discuss how they can reveal the affordances of different techniques, and how they could lead to a deeper understanding of the underlying algorithms. We describe a proof-of-concept application to visualize and manipulate Hidden Markov Models, that provides a ground for a broader discussion on the potentials and challenges of interactive visualizations in human-centered machine learning.Download Project Page,