학술논문
DMLR: Data-centric Machine Learning Research -- Past, Present and Future
Document Type
Working Paper
Author
Oala, Luis; Maskey, Manil; Bat-Leah, Lilith; Parrish, Alicia; Gürel, Nezihe Merve; Kuo, Tzu-Sheng; Liu, Yang; Dror, Rotem; Brajovic, Danilo; Yao, Xiaozhe; Bartolo, Max; Rojas, William A Gaviria; Hileman, Ryan; Aliment, Rainier; Mahoney, Michael W.; Risdal, Meg; Lease, Matthew; Samek, Wojciech; Dutta, Debojyoti; Northcutt, Curtis G; Coleman, Cody; Hancock, Braden; Koch, Bernard; Tadesse, Girmaw Abebe; Karlaš, Bojan; Alaa, Ahmed; Dieng, Adji Bousso; Noy, Natasha; Reddi, Vijay Janapa; Zou, James; Paritosh, Praveen; van der Schaar, Mihaela; Bollacker, Kurt; Aroyo, Lora; Zhang, Ce; Vanschoren, Joaquin; Guyon, Isabelle; Mattson, Peter
Source
Subject
Language
Abstract
Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure development for the creation of next-generation public datasets that will advance machine learning science. We chart a path forward as a collective effort to sustain the creation and maintenance of these datasets and methods towards positive scientific, societal and business impact.
Comment: Published in the Journal of Data-centric Machine Learning Research (DMLR) at https://data.mlr.press/assets/pdf/v01-5.pdf
Comment: Published in the Journal of Data-centric Machine Learning Research (DMLR) at https://data.mlr.press/assets/pdf/v01-5.pdf