20 citations · 20 across the 2 of their papers we have counts for
2 papers
cs.HC2023★ 20 cited
Angler: Helping Machine Translation Practitioners Prioritize Model Improvements
Samantha Robertson, Zijie J. Wang, Dominik Moritz +2
Machine learning (ML) models can fail in unexpected ways in the real world, but not all model failures are equal. With finite time and resources, ML practitioners are forced to pri…
cs.HC2023
Collaborative Machine Learning Model Building with Families Using Co-ML
Tiffany Tseng, Jennifer King Chen, Mona Abdelrahman +4
Existing novice-friendly machine learning (ML) modeling tools center around a solo user experience, where a single user collects only their own data to build a model. However, solo…