44 citations · 98 across the 10 of their papers we have counts for
4 papers · 1 filter
Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation
Bhavya Ghai, Q. Vera Liao, Yunfeng Zhang +1
We propose a new active learning (AL) framework, Active Learning++, which can utilize an annotator's labels as well as its rationale. Annotators can provide their rationale for cho…
NAS-Navigator: Visual Steering for Explainable One-Shot Deep Neural Network Synthesis
Anjul Tyagi, Cong Xie, Klaus Mueller
Recent advancements in the area of deep learning have shown the effectiveness of very large neural networks in several applications. However, as these deep neural networks continue…
Measuring Social Biases of Crowd Workers using Counterfactual Queries
Bhavya Ghai, Q. Vera Liao, Yunfeng Zhang +1
Social biases based on gender, race, etc. have been shown to pollute machine learning (ML) pipeline predominantly via biased training datasets. Crowdsourcing, a popular cost-effect…
Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience
Bhavya Ghai, Q. Vera Liao, Yunfeng Zhang +2
The wide adoption of Machine Learning technologies has created a rapidly growing demand for people who can train ML models. Some advocated the term "machine teacher" to refer to th…