Zhuanyi Huang

Dustin Arendt, Zhuanyi Huang, Prasha Shrestha et al.

Evaluation beyond aggregate performance metrics, e.g. F1-score, is crucial to both establish an appropriate level of trust in machine learning models and identify future model improvements. In this paper we demonstrate CrossCheck, an interactive visualization tool for rapid crossmodel comparison and reproducible error analysis. We describe the tool and discuss design and implementation details. We then present three use cases (named entity recognition, reading comprehension, and clickbait detection) that show the benefits of using the tool for model evaluation. CrossCheck allows data scientists to make informed decisions to choose between multiple models, identify when the models are correct and for which examples, investigate whether the models are making the same mistakes as humans, evaluate models' generalizability and highlight models' limitations, strengths and weaknesses. Furthermore, CrossCheck is implemented as a Jupyter widget, which allows rapid and convenient integration into data scientists' model development workflows.

Lyndsey Franklin, Kyungsik Han, Zhuanyi Huang et al.

We present the results of a user study with novice NMR analysts (N=19) involving a gamified simulation of the NMR analysis process. Participants solved randomly generated spectrum puzzles for up to three hours. We used eye tracking, event logging, and observations to record symptoms of cognitive depletion while participants worked. Analysis of results indicate that we can detect both signs of learning and signs of cognitive depletion in participants over the course of the three hours. Participants' break strategies did not predict or reflect game scores, but certain symptoms appear predictive of breaks.