Understanding How Humans Inject Knowledge into Machine Learning Workflows through Visual Analytics
Visual analytics (VA) plays an increasingly important role in supporting machine learning (ML) workflows. In the field of visualization, such approaches and techniques are referred to as VIS4ML. While ML models are mostly learned automatically, the corresponding ML workflows receive a variety of human inputs, such as data labelling, feature engineering, model architecture designing, hyper-parameter tuning, and so on. In this work, we surveyed over 200 VIS4ML papers to gain an understanding of how humans inject their knowledge into ML workflows through interactive visualization. We collected a corpus of VIS4ML papers from the IEEE VIS conferences in the past decade. We developed a coding s...
| Authors | Yiwen Xing, Philip Beaucamp, Joyraj Chakraborty et al. |
|---|---|
| Primary category | cs.HC |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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