DMLR：以数据为中心的机器学习研究——过去、现在与未来
机器学习
2024-06-04 v2 人工智能
分布式、并行与集群计算
信号处理
摘要
借鉴ICML 2023首届DMLR研讨会及先前会议的讨论，我们在本报告中概述了社区参与和基础设施发展对于创建推进机器学习科学的下一代公共数据集的相关性。我们规划了一条前进道路，作为维持这些数据集和方法的创建与维护的集体努力，以实现积极的科学、社会与商业影响。
关键词
引用
@article{arxiv.2311.13028,
title = {DMLR: Data-centric Machine Learning Research -- Past, Present and Future},
author = {Luis Oala and Manil Maskey and Lilith Bat-Leah and Alicia Parrish and Nezihe Merve Gürel and Tzu-Sheng Kuo and Yang Liu and Rotem Dror and Danilo Brajovic and Xiaozhe Yao and Max Bartolo and William A Gaviria Rojas and Ryan Hileman and Rainier Aliment and Michael W. Mahoney and Meg Risdal and Matthew Lease and Wojciech Samek and Debojyoti Dutta and Curtis G Northcutt and Cody Coleman and Braden Hancock and Bernard Koch and Girmaw Abebe Tadesse and Bojan Karlaš and Ahmed Alaa and Adji Bousso Dieng and Natasha Noy and Vijay Janapa Reddi and James Zou and Praveen Paritosh and Mihaela van der Schaar and Kurt Bollacker and Lora Aroyo and Ce Zhang and Joaquin Vanschoren and Isabelle Guyon and Peter Mattson},
journal= {arXiv preprint arXiv:2311.13028},
year = {2024}
}
备注
Published in the Journal of Data-centric Machine Learning Research (DMLR) at https://data.mlr.press/assets/pdf/v01-5.pdf