A Case for Rejection in Low Resource ML Deployment

Autor:	White, Jerome, Madaan, Pulkit, Shenoy, Nikhil, Agnihotri, Apoorv, Sharma, Makkunda, Doshi, Jigar
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition
Zdroj:	NeurIPS 2022 workshop on Challenges In Deploying And Monitoring Machine Learning Systems
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2208.06359 Zobrazit plný text záznamu View this record from Arxiv