论文标题
低资源ML部署中拒绝的案例
A Case for Rejection in Low Resource ML Deployment
论文作者
论文摘要
构建可靠的AI决策支持系统需要一组强大的数据来培训模型；在数量和多样性方面。在资源有限的设置或在部署的早期阶段中，获取此类数据集可能很困难。样本拒绝是应对这一挑战的一种方法，但是对于此类情况，该领域的许多现有工作都不适合。本文证明了该立场并提出了一个简单的解决方案作为概念基线证明。
Building reliable AI decision support systems requires a robust set of data on which to train models; both with respect to quantity and diversity. Obtaining such datasets can be difficult in resource limited settings, or for applications in early stages of deployment. Sample rejection is one way to work around this challenge, however much of the existing work in this area is ill-suited for such scenarios. This paper substantiates that position and proposes a simple solution as a proof of concept baseline.
