The Science of Rejection: A Research Area for Human Computation
Not on the map. Why?
Abstract (arXiv)
We motivate why the science of learning to reject model predictions is central to ML, and why human computation has a lead role in this effort.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 18 references that are in the map. First posted on arXiv 11 Nov 2021.
Builds on
9 of 18 references.
References · 9
- 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
- 2013Intriguing properties of neural networkscited by 5,155
- 2017On Calibration of Modern Neural Networkscited by 4,960
- 2019When Does Label Smoothing Help?cited by 988
- 2019Measuring Calibration in Deep Learningcited by 446
- 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 253
- 2020Learning to Complement Humanscited by 124
- 2019The Algorithmic Automation Problem: Prediction, Triage, and Human Effortcited by 101
- 2016Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Explorationcited by 26
Built on by
Citing papers per year: 2022: 1.
1 paper.