DMLR: Data-centric Machine Learning Research -- Past, Present and Future
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| 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 |
| author_facet | 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_13028 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DMLR: Data-centric Machine Learning Research -- Past, Present and Future 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 Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing Signal Processing 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. |
| title | DMLR: Data-centric Machine Learning Research -- Past, Present and Future |
| topic | Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing Signal Processing |
| url | https://arxiv.org/abs/2311.13028 |